The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis

The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis Yi Mou, Jiaxi Li, Zijun Ke, Xueyan Li, Bo Zhang, Yini Liao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5667808/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Nature Human Behaviour → Version 1 posted You are reading this latest preprint version Abstract The fundamental characteristics of mathematical ability in individuals with Autism Spectrum Disorder (ASD), specifically proficiency level and variability, remain inadequately understood. This meta-analysis reveals that individuals with ASD exhibit significantly lower math scores (Hedge’s g = -0.181/-0.592) and greater variability (natural logarithm of variability ratio, lnVR = 0.179/0.272) compared to the general population, as represented by norms of standardized math tests ( M = 100, SD = 15; 3,051 participants) and typically developing (TD) control groups (2,351 participants). Group discrepancies in proficiency and variability were moderated by intelligence, age, or their interactions. The moderate math-intelligence relationship in the population with ASD provides a theoretical framework for studying their math abilities. Additionally, the discrepancy in math proficiency between the ASD and TD groups increases over the past four decades. These findings underscore the need for sustained, individualized mathematical education for individuals with ASD, and the importance of investigating the developmental trajectories of mathematical skills in ASD. Social science/Psychology/Human behaviour Social science/Education autism spectrum disorder mathematical ability intelligence Figures Figure 1 Introduction Autism spectrum disorder (ASD) is a neurodevelopmental disorder, characterized by major deficits in social communication and interaction, and/or repetitive or ritualized behaviors (APA, 2013). The growing population with ASD needs specialized educational support to foster academic achievement (Bullen et al., 2020; Humphrey, 2010 ). Mathematics is an essential subject closely related to academic achievement and life success (Claessens & Engel, 2013 ; Duncan et al., 2007 ), making it important for understanding the academic profiles of ASD. Despite this importance, the math abilities of individuals with ASD remain understudied (Mayes et al., 2019; Keen et al., 2016 ). Two fundamental aspects of mathematics ability in this population - proficiency (strength) and variability (heterogeneity) - have not been fully characterized. It remains unclear whether individuals with ASD exhibit comparable math proficiency and variability to the general population (Keen et al., 2016 ) and what factors may contribute to these group discrepancies, if there are. This knowledge gap hinders the development of a robust theoretical framework for the math abilities of ASD. For instance, while some theories suggest that individuals with ASD may exhibit strengths in math (Baron-Cohen, 2007), others posit that they may face challenges (e.g., cognitive dysfunction theories; Happé, 1999 ). Existing research needs to be synthesized to resolve these conflicting perspectives and provide a foundation for understanding their math abilities. Furthermore, clarifying these fundamental characteristics is essential for informing targeted educational interventions aimed at optimizing academic outcomes for the ASD population. Proficiency and variability in math ability Previous studies have reported highly inconsistent findings on the math proficiency of individuals with ASD. Some studies find that individuals with ASD, ranging from preschoolers to adults, presented comparable scores to TD control groups in standardized math tests (e.g., Mayes & Calhoun 2008; Tops et al., 2017) and specific mathematics tasks, such as non-symbolic numerical comparison, verbal numerical estimation, counting, and symbolic arithmetic (e.g., Titeca et al., 2014 ; Turi et al., 2015 ). A few studies have even highlighted their superior math abilities in calendar calculation (O'Connor et al. 2000 ), number estimation and decomposition (Sacks, 1995 ; Soulières et al., 2010 ), and computation (Cowan & Frith, 2009 ; Soulières et al., 2010 ). However, individuals with ASD also exhibit lower scores on standardized math tests (e.g., Bullen et al., 2020; Wei et al., 2011 , 2013 , 2014 ) and specific math tasks such as counting (Jarrold & Russell, 1997 ), verbal number estimation (Meaux et al., 2014 ), and non-symbolic and symbolic number representation (Aagten-Murphy et al., 2015; Li et al., 2023). Additionally, a higher proportion of individuals with ASD had learning disabilities in math (e.g., 22% in Oswald et al., 2016) compared to the general population (e.g., 5–10%; Geary, 2004 ; Mazzocco & Thompson, 2010 ). Understanding the variability, or heterogeneity, in math abilities among individuals with ASD is equally crucial because it can enhance insight into individual differences of their cognitive profiles and informs the development of individualized educational strategies (Wang et al., 2020 ; Yakubova et al., 2020 ). Some studies report that individuals with ASD exhibit larger or smaller standard deviations in math scores, compared to TD groups (Aagten-Murphy et al., 2015; Chen et al., 2019 ; Gagnon et al., 2004; Hiniker et al., 2016), but most of them simply presented the variabilities without statistically comparing them between groups, leaving an open question about if individuals with ASD indeed exhibit greater math variability than the general population. To date, two meta-analyses have examined the math abilities of individuals with ASD reporting that this population scores below the normative average on standardized tests (M norm = 100, Cohen’s d = 0.2; Chiang & Lin, 2007 ) or their TD peers (Hedges’ g = 0.49; Tonizzi & Usai, 2023 ). While these studies provide valuable insights, they have several limitations. First, the scope of these meta-analyses was limited in sample size and temporal range (e.g., including only eight empirical studies published before 2004, or 13 studies published between 2013 and 2020), hindering a comprehensive understanding of the state of research in this field and potential changes in math abilities among individuals with ASD over the past several decades. Second, they focused on math proficiency without accounting for variability within the ASD population. Third, they compared the ASD groups’ math scores to either normative averages or actual TD control groups, rather than both. Comparisons to normative averages provide a baseline for understanding where individuals with ASD stand relative to the norm, and comparisons to TD control groups, on the other hand, control for more confounding variables such as age, intelligence, and demographic factors, allowing for a clearer attribution of math performance discrepancies to autism-specific factors rather than unrelated variables. Fourth, although one meta-analysis (Tonizzi & Usai, 2023 ) explored the influence of certain cognitive (e.g., intelligence) and demographic factors (e.g., age) on group differences in math ability, these analyses were constrained by limited distributions (e.g., age range: 9.39–14.88 years). This restricted range precluded a more nuanced examination of how these factors interact to influence math performance. Furthermore, important non-cognitive factors, such as ASD symptom severity, were not considered despite evidence suggesting their relevance (Chen et al., 2019 ; Oswald et al., 2016). Addressing these limitations by examining multiple cognitive and non-cognitive moderators across broader samples and timeframes is critical. Moderators Intelligence One key factor in understanding math ability is the relationship between math and intelligence, as this sheds light on how general cognitive abilities contribute to and interact with math development (Peng et al., 2019 ; Geary, 2011 ). A moderate math-IQ relationship is typically observed in the general population, although the strength of the correlation varies depending on the component of intelligence (Peng et al., 2019 ; Postlethwaite, 2011 ). These components include verbal intelligence (or crystallized intelligence), non-verbal intelligence (or fluid intelligence), and general intelligence (a composite of both verbal and non-verbal abilities; Cattell, 1963 ; Wechsler, 2009 ; Woodcock-Johnson, 1977). In the ASD population, however, the math-IQ relationship remains unclear. While some studies found the positive math-IQ link in the ASD population, even after controlling for factors such as ASD severity, adaptive behaviors, or test anxiety, other studies found no significant correlations (Table S1 ). Additionally, several studies have highlighted their deviation, with some individuals exhibiting either superior or weaker math proficiency despite having average intelligence, resulting in substantial differences between math and IQ scores (Estes et al., 2011; Jones et al., 2009; Kim et al., 2017). These findings suggest that their math abilities may not be reliably predicted by IQs. The math-IQ relationship in ASD can be investigated through various approaches. One method involves pooling the reported math-IQ correlation coefficients, as well as assessing the math-IQ deviation (i.e., the difference between math and IQ scores) across studies to determine the overall relationship. Another approach is to examine whether intelligence moderates potential discrepancies in math proficiency between individuals with ASD and the general population. Specifically, if a positive math-IQ relationship exists in the ASD population, we would expect math scores - relative to the normative average - to increase as intelligence rises. Additionally, it is critical to explore whether intelligence moderates the potential math proficiency discrepancy between individuals with ASD and TD groups, extending the analysis beyond normative averages to control for participants with a range of math abilities. This also allows to examine whether the math proficiency discrepancy between ASD and TD groups differs at various levels of intelligence. Previous research has shown that individuals with ASD who exhibit higher verbal intelligence demonstrate smaller discrepancies in math proficiency relative to their TD peers (Tonizzi & Usai, 2023 ). However, the limited range of intelligence scores (96–117) in this analysis raises the question of whether this moderating effect persists across a broader spectrum of intelligence levels. Furthermore, neither this research nor any others have explored whether intelligence moderates the variability in math abilities between individuals with ASD and the general population. Greater math variability in ASD groups may persist across different intelligence levels, or increases as intelligence rises, potentially due to reduced cognitive constraints in these individuals, allowing non-intelligence-related factors to exert a stronger influence (Chen et al., 2018; Tonizzi & Usai, 2024 ). Alternatively, the group discrepancy in variability may reduce with increasing intelligence, as high intelligence may enable individuals with ASD to mitigate cognitive challenges, such as difficulties with executive functioning (Pennington & Ozonoff, 1996 ), resulting in more consistent and predictable pattern of math performance. These hypotheses warrant empirical investigation, and examining whether and how math variability in individuals with ASD is related to intelligence could provide indispensable insights into the math-IQ relationship within this population. Age As the challenges of learning math typically increase with age, it is necessary to explore whether math abilities of individuals with ASD change as they grow, compared to those of the general population. Previous findings are inconsistent: compared to TD peers, individuals with ASD show either comparable growth in math proficiency with age (Hiniker et al., 2016; Kim et al., 2017), or a slower increase that results in enlarged group discrepancies during development (Wang et al., 2022; Wei et al., 2013 ). A study found that school-aged individuals with ASD (9–15 years) exhibit a larger discrepancy in math proficiency compared to their TD peers as they age (Tonizzi & Usai, 2023 ). However, it remains unclear whether this trend persists across a broader age range, including preschool to adulthood. If the risk of falling further behind TD peers continues throughout the lifespan, this underscores the importance of providing early and continuous support for these individuals. Even less is known about whether math variability between groups varies with age, as no studies have quantitatively examined whether the group discrepancy in math variability (e.g., standard deviations of math performance) between individuals with ASD and the general population changes across different age groups. Given that variability in certain cognitive abilities and other developmental domains, such as executive functions (Pellicano, 2012 ), and social communication skills (Wallace et al., 2017 ), tends to broaden with age in individuals with ASD, it is plausible that similar trends may emerge in their math abilities (Charman et al., 2011 ). As a result, the discrepancy in math variability between individuals with ASD and the general population may widen over time. However, this hypothesis remains to be empirically tested. While a previous study explored the moderating effects of age and intelligence on the group discrepancy in math separately (Tonizzi & Usai, 2023 ), their interaction has not been examined. Investigating this interaction could reveal whether individuals with ASD at different intelligence levels exhibit distinct developmental trajectories in math ability over time. Previous research descriptively indicates that ASD children with higher intelligence tend to exhibit better math proficiency and less variability than those with lower intelligence, with these distinctions becoming even more marked from childhood to adolescence (Kim et al., 2017). Quantifying the interactive effects of intelligence and age on math performance would provide a more developmental perspective on the role of cognitive factors in math learning for individuals with ASD. Symptom Severity Individuals with ASD present a high degree of heterogeneity in the severity of symptoms (APA, 2013). More severe symptoms are often associated with weaker social and cognitive competence and an increase in problem behaviors (APA, 2013), which may collectively contribute to more difficulties in academic learning, including mathematics. However, while some studies found a significant correlation between ASD symptom severity, as measured by tools like the Autism Diagnostic Observation Schedule (ADOS; Center for Disease Control, 2020b ), and math proficiency, others have not (e.g., Miller et al., 2016 ; Zaidman-Zait et al., 2020), highlighting the need for meta-analyses to further explore these relationships. Moreover, autism severity may also be associated with math variability, as more severe symptoms are often accompanied by a higher prevalence of comorbid conditions (Gadke et al., 2016 ) and increased problem behaviors (Lindor et al., 2019 ), differentially impacting math learning and resulting in greater variability. Conversely, severe autism symptoms might correspond to more substantial cognitive and non-cognitive challenges and the need for interventions focused on primary life or social skills (Gadke et al., 2016 ; Lindor et al., 2019 ). These may limit opportunities to engage in math learning and lead to reduced variability in math abilities. These hypotheses remain speculative and require further investigation. Publication Year The evolution of autism as a diagnostic category since its formal recognition in the DSM III has been marked by significant changes in diagnostic criteria, intervention strategies, public perception, and social support systems (APA, 1980; 2013). These alterations have broadened the autism spectrum, potentially impacting the academic performance and heterogeneity of individuals with ASD over time (Charman et al., 2011 ). Additionally, more individuals with ASD are receiving inclusive education, and targeted interventions specific to math have also increased in recent years (Gevarter et al., 2016 ; Roberts & Simpson, 2016 ; Root et al. 2021 ), which may have a sustained positive impact on their math performance. However, there may be little substantial change in their math ability over past decades, as math ability is not a core diagnostic criterion or a primary target of interventions for most individuals with ASD. Notably, this oversight may disadvantage them and exacerbate the discrepancies in math abilities compared to the general population, particularly as math skills have become increasingly emphasized and training has been enhanced for the general population in recent years (Wang et al., 2023 ). Therefore, it remains unknown whether and how the math proficiency and variability of individuals with ASD have changed over time. The current study This meta-analysis was conducted to characterize two fundamental features of math ability—proficiency and variability—of individuals with ASD, and to compare these features with those of the general population. Previous studies used various math assessments, including standardized math tests (e.g., Woodcock-Johnson III Test of Achievement, WJ-III-ACH, Mather & Woodcock, 2001 ) and non-standardized tasks measuring specific math aspects. To ensure a comprehensive analysis, all these types of math assessments were included in this meta-analysis. We compared individuals with ASD to the general population, represented by both the norms of standardized math tests (typically normed by age) and actual TD control groups that often matched the ASD group in multiple factors (e.g., age, intelligence, or socioeconomic factors). Furthermore, we examined whether the potential group discrepancies in math was moderated by factors including intelligence, age, ASD symptom severity, and publication year. Moreover, given the potential interaction between intelligence and age on the development of math abilities in individuals with ASD (Kim et al., 2017; Wei et al., 2013 ), we also examined this interaction in meta-analyses. Results Group discrepancies in math proficiency between ASD groups and norms Eighty-seven samples from 47 studies reported standardized math test scores, involving 3051 participants with ASD. The sample sizes of these studies ranged from 3 (Stroizer et al., 2015) to 164 (Mayes et al., 2019). The groups with ASD exhibited lower math scores ( M mean = 95.97, SD mean = 16.87; Range mean = 53.30-138.31) compared to the normative average of standardized math tests ( M = 100; Hedges’ g = − .327, 95%CI = [-.565, − .089], p = .008, k = 87). Their average IQs were 99.47, 97.59, and 99.44 for full-scale, verbal, and non-verbal IQs, respectively. Meta-regressions revealed that the group discrepancy was moderated by three types of IQs (full-scale IQ: B = .049, 95%CI = [.032, .066], p < .001; verbal IQ: B = .048, 95%CI = [.036, .060], p < .001; non-verbal IQ: B = .053, 95%CI = [.047, .059], p < .001), suggesting that the difference in math scores between the ASD group and the norm decreased as the IQs of the ASD group increased. None of the other moderators or interactions showed significant effects. Group discrepancies in math variability between ASD groups and norms The standard deviation of math scores of groups with ASD ( M SD = 17.75, SD SD = 4.79; Range SD = 4.04–32.26) was 1.185 = exp (0.170) times greater than that of the norm ( SD = 15; lnVR = 0.170, 95% CI = [.112, .228], p < .001, k = 87). This group discrepancy was moderated by full-scale IQ and age of the ASD group (full-scale IQ: B = − .004, p = .043; age: B = .018, p = .008), suggesting that the group discrepancy decreased when the ASD group had the higher IQ, and increased with age. There was no significant effect of other moderators. There is also a significant interaction effect between verbal IQ and age ( B = − .238, 95%CI = [-.445, − .031], p = .031, k = 47). Verbal IQ scores ranged from 63.00 to 117.31, with an average of 99.22. For the ASD group with verbal IQ scores below the mean, the slope of age was 0.249 ( SE = 0.015, p < .001, simple intercept = 0.231, age = 4.50-26.62 years; k = 25); for the ASD group with verbal IQ scores above the mean, the age slope was − 0.227 ( SE = 0.020, p < .001, simple intercept = 0.095, age = 8.27–26.40; k = 22). There was a significant difference between the slopes of the two IQ groups ( p < .001), suggesting that math group discrepancy increased with age for the lower verbal-IQ groups but reduced with age for the higher verbal-IQ ones. Group discrepancies in math proficiency between ASD and TD groups The following results were from 66 samples of 33 studies, involving 2351 participants with ASD, and 1851 TD participants (Table S1 ). The ASD group performed worse in math than the TD control group ( Hedges’ g = − .676, SE = .141, 95%CI = [-.962, − .390], p < .001, k = 66). This group discrepancy in math scores was reduced when all three types of IQs of the ASD group increased or as the full-scale and non-verbal IQs of the TD group decreased (Table 1). More analyses on the math-IQ relationship including correlations and differences are presented in Supplementary Materials. Additionally, this group discrepancy increased as the year of publication became more recent ( B = − .031, 95%CI = [-.055, − .006], p = .019). The group discrepancy in math scores may be attributable to the differences in IQ scores, given that the ASD group had lower IQ scores across all three types than the TD group ( Hedges’ g = − .379/-.912/-.374; SE = .111/.253/.146, 95%CI = [-.612, − .145]/ [-1.441, − .383]/ [-.680, − .069], p = .003/.002/.020, k = 39/41/40 for full-scale, verbal, and non-verbal IQs, respectively). To account for this difference, we statistically adjusted the ASD group’s IQ scores to match those of the TD group, but found that the ASD group still scored lower in math tests (intercept = − .237/-.215/-.232, SE = .101/.101/.092, p = .034/.045/.021). Furthermore, the group discrepancy was moderated by the interaction between full-scale IQ and age ( B = − .159, SE = .044, 95%CI = [-.274, − .045], p = .016, k = 48). Full-scale IQ scores ranged from 89.88 to 120.25, with the average at 102.48. For the ASD group with full-scale IQ below the mean, the slope of age was 0.134 ( SE = 0.017, p < .001, simple intercept = -0.748, age = 5.20–26.40 years; k = 22); for the ASD group with the IQ above the mean, the slope of age was − 0.186, ( SE = 0.018, p < .001, simple intercept = -0.178, age = 5.98–26.40; k = 26). There was significant difference between the slopes of the two IQ groups ( p < .001), suggesting that group discrepancy of math proficiency decreased with age for the lower full-scale IQ groups but increased with age for the higher IQ ones. No moderator effect on the group discrepancy in proficiency was found in symptom severity, age, or publication year (Table 1). Group discrepancies in math variability between ASD and TD groups The standard deviation of math scores of the ASD group ( M SD = 13.78, SD SD = 9.67; Range SD = 0.05–49.54, K = 66) was 1.365 = exp (0.311) times greater than that of the TD group ( M SD = 11.31, SD SD = 8.45; Range SD = 0.01 to 49.66, k = 66; lnVR = .311, SE = .052, p < .001, 95% CI = [.207, .417]; Table 1). The group discrepancy was not moderated by any moderators or the interactions between IQ scores and age (Table 1). The ASD group still exhibited larger variability of math scores than the TD group when their full-scale, verbal and non-verbal IQ scores were statistically adjusted to match those of the TD group (intercept = .254/.210/.250; SE = .052/.046/.060, p s < .001, k = 46/44/43). Discussion Our meta-analysis reveals that individuals with ASD overall exhibited significantly lower proficiency and greater variability in math ability compared to the general population as represented by norms of standardized math tests and the actual TD control groups included in each primary study. Therefore, individuals with ASD have unique educational needs in math, which should inform both the design of specialized educational interventions and the placement of students within appropriate educational settings. Moreover, this study identifies several factors moderating these group discrepancies, further elucidating the underlying reasons for these fundamental characteristics of the ASD population. Math proficiency within the ASD groups, compared to the normative average, increases across all three IQ components (full-scale, verbal, and non-verbal IQ), reflecting a positive math-IQ relationship within the ASD population. This relationship is further supported by the moderate math-IQ correlations comparable to those in the general population (Peng et al., 2019 ). Additionally, the math-IQ correlations and math-IQ deviation score do not differ between the ASD and TD groups, also suggesting that overall, the math abilities of individuals with ASD align with their intelligence (Supplementary Materials). When compared with TD groups, the math proficiency of ASD groups also approaches that of TD groups as IQ levels increase. However, a group discrepancy persists even when both groups are statistically adjusted to the same IQ levels, suggesting that factors beyond intelligence influence math proficiency in individuals with ASD (see discussion below). Notably, our meta-analysis revealed that ASD groups, on average, had lower IQs than TD groups, indicating that many studies fail to rigorously control for this variable, representing a significant methodological issue in the current body of research. Although math proficiency generally increases with intelligence, ASD individuals with different intelligence levels exhibit distinct developmental trajectories in math with age, as reflected in the interaction between full-scale IQ and age. Specifically, ASD individuals with lower full-scale IQs (89.88-100.89) show an increasing convergence in math proficiency with their TD peers over time. This trend suggests that ASD individuals with average intelligence levels can benefit from long-term math education similarly to TD peers. However, ASD individuals with higher full-scale IQs (103.70–120.25) demonstrate a widening gap in math proficiency as they age relative to their TD peers. Although these ASD individuals also benefit from math education, they may encounter greater challenges in math learning compared to their TD peers with similar levels of intelligence. As the content of math one needs to learn becomes more complicated and challenging with age, obtaining advanced math knowledge requires additional abilities beyond intelligence, such as social communication and cooperation, academic motivation, self-efficacy, and mental pressure management (e.g., Ahmed et al., 2010 ; Rosenfeld et al., 2000 ). While TD individuals with above-average intelligence may have stronger proficiency in these non-cognitive aspects (Diseth et al., 2014 ; Mohzan et al., 2013 ), individuals with ASD, even those with average or above-average intelligence, may struggle in these areas and even experience more pressures and challenges in daily life as they age (Adreon et al., 2007; Anderson et al., 2017 ). These disparities in non-cognitive skills may amplify the advantages TD individuals with higher intelligence have in mastering complex math concepts, whereas ASD individuals tend to fall further behind due to their relative disadvantages in these areas. This finding underscores the importance of providing life-span educational support tailored to the needs of ASD individuals with average or above-average intelligence, aiming to optimize their academic outcomes and prevent their math learning needs from being overlooked (Matson et al., 2016 ). While our findings highlight the moderating effects of age and its interaction with intelligence, it should be noted that most data are derived from cross-sectional studies rather than longitudinal ones, which are scarce in this field (Kim et al., 2017; May et al., 2015; Wei et al., 2013 , 2014 ). More longitudinal data are needed to control for interindividual differences and sociodemographic factors and clarify the developmental trajectory of math abilities in individuals with ASD. Notably, the discrepancy in math proficiency between the ASD and TD groups has widened over the past few decades, signaling concerns about the effectiveness of math and cognitive interventions for individuals with ASD. While interventions have primarily focused on social interaction, language development, and behavioral issues, the enhancement of math skills has often been neglected. While the general population has benefited from more intensive and comprehensive math education, individuals with ASD may have been overlooked in this regard, placing them at a distinct disadvantage. Additionally, the widening group discrepancy over time may also reflect changes in research methodologies over the past decades (Adams et al., 2023 ; Philip et al., 2012 ). For example, recent studies may employ more rigorous recruitment strategies for TD samples, ensuring they are more closely matched to ASD samples on a range of factors. This, in turn, enhances the sensitivity of research tools in detecting group discrepancies. When compared to standardized math test norms (SD = 15), the variability in math performance within the ASD groups decreased as full-scale IQ increased. This suggests that individuals with higher IQs may experience fewer cognitive limitations, resulting in more stable math performance. Additionally, their math variability tended to increase with age. This may be due to that some individuals are able to manage the challenges of the growing complexity of math content as individuals mature, whereas others struggle more significantly as the demands of the subject matter increase. Furthermore, the increasing math variability may also reflect the diverse educational environments and varying intervention approaches encountered by individuals with ASD as they age (Kurth & Mastergeorge, 2010 ; Lewis & van Schalkwyk, 2020 ). Importantly, the developmental trajectories of math variability within the ASD groups differ according to both age and intelligence. Specifically, ASD individuals with lower verbal IQs (63.00–99.07; including individuals with average intelligence and intellectual disabilities) exhibit greater math variability as they aged, while those with higher verbal IQs (99.48–117.31) show reduced variability. In terms of the math-IQ relationship, some individuals in the groups with lower verbal IQs demonstrate math abilities comparable to the general population, while others face considerable difficulties in math. These differences likely become more pronounced as these individuals age, with increasing math demands. Moreover, individuals in these groups may face broader life challenges, both in academic settings and beyond (e.g., Schneider & Niklas, 2017 ; Tonizzi & Usai, 2023 ). In contrast, ASD individuals with higher verbal IQs may experience fewer difficulties in math, as well as everyday life, and show greater alignment with their TD peers. Nevertheless, the math variability remains greater in individuals with ASD compared to TD controls, even when the IQs are matched between groups. Moreover, this ASD-TD group discrepancy was not moderated by any moderators included in this study, suggesting that the math variability of the individuals with ASD may be influenced by factors beyond intelligence, age, symptom severity and publication year. Non-cognitive factors, such as communication challenges, anxiety, and emotional issues (Maskey et al., 2013 ), and comorbidities (e.g., ADHD, depression, epilepsy, Craig et al., 2015 ; Gadke et al., 2016 ; Viscidi et al., 2013) may increase their math variability (Table S1 ). However, due to the limited number of studies addressing these factors, they were not included in our analysis. More data on these factors should be accumulated to provide a comprehensive understanding of math abilities in the ASD population. ASD symptom severity did not moderate the group discrepancies in math proficiency or variability, consistent with previous findings (Hiniker et al., 2016; Iuculano et al., 2020; Miller et al., 2016 ; Zaidman-Zait et al., 2020). However, the use of composite ADOS scores may obscure relationships between math abilities and specific autism components (e.g., social affect, and restricted and repetitive behaviors). Additionally, the narrow range of ADOS scores across studies (group average ranged from 6.00 to 8.23) and the underrepresentation of participants with severe symptoms may limit the power to detect potential relationships. Collecting data from this subgroup is particularly challenging, highlighting the need to improve current assessments to better accommodate these individuals (Wang et al., 2023 ). Nevertheless, the relationship between math proficiency and ASD symptom severity remains insufficiently documented. This study provides two key insights that should be central to theoretical models addressing the math abilities of individuals with ASD: their lower proficiency and greater variability in math, as well as the positive math-IQ relationship. While some theories highlight the strengths of math-related abilities in individuals with ASD (e.g., Baron-Cohen, 2007), our findings present a more nuanced perspective. We acknowledge that some individuals with ASD exhibit strong math abilities, but on the whole, their math proficiency is weaker than that of the general population, even when IQ levels are comparable. Moreover, the ASD-TD group discrepancy in math proficiency has widened over recent decades, raising concerns about the math development of the ASD population. Importantly, our analysis focuses on individuals with ASD who are able to engage with math tasks. However, a substantial proportion of individuals with ASD are unable to engage with such cognitive assessments, meaning that the general pattern of weaker math abilities is more representative of the broader ASD population. Our study is also the first meta-analysis to demonstrate that individuals with ASD exhibit greater math variability than the general population. This parallels the substantial heterogeneity seen across many other domains within ASD such as social communication (Fountain et al., 2012), adaptive behaviors (Szatmari et al., 2015 ), language (Zheng et al., 2020 ), cognitive processes (Charman et al., 2011 ), and motor function (Fournier et al., 2010 ). This finding may help reconcile previous inconsistent results regarding the strengths and weaknesses of math abilities in this population. Furthermore, the math variability remains greater in individuals with ASD, even when their intelligence or age are matched with TD groups, suggesting that additional factors, such as non-cognitive factors, comorbidities, or educational environments, may contribute to this variability. Furthermore, the math-IQ relationship is critical within any theoretical framework for understanding the math abilities of individuals with ASD. While some individuals may demonstrate math-IQ deviations (e.g., Aagten-Murphy et al., 2015; Jones et al., 2009), our findings provide evidence for a moderate positive math-IQ relationship in the ASD population, comparable to that in the general population. This suggests that theoretical frameworks and interventions based on the math-IQ relationship in the general population may also apply to individuals with ASD. Importantly, our findings expand the understanding of the math-IQ relationship by adopting a more refined, developmental perspective. We examined this relationship across subgroups with varying intelligence levels and considered the complex interactions between cognitive and non-cognitive factors, as well as educational influences. Individuals with ASD exhibit distinct developmental trajectories in math proficiency and variability depending on their intelligence level. Specifically, individuals with lower intelligence (most within the average range) tend to approximate the math proficiency of their TD peers over time while displaying increasing variability. In contrast, individuals with average or above-average intelligence exhibit a larger gap in math proficiency relative to similarly intelligent TD peers, along with reduced math variability as they age. We propose that intelligence plays a crucial role in supporting math learning in individuals with ASD, similar to its role in the general population. The support of cognitive abilities enables these individuals to benefit from long-term education, and higher intelligence may also help stabilize math performance by mitigating the negative effects of non-cognitive factors and environmental challenges. However, individuals with average or above-average intelligence may still experience limitations due to non-cognitive factors such as difficulties in social communication, emotional regulation, and behavioral challenges (Konstantareas & Stewart, 2006 ; Nyrenius & Billstedt, 2020 ; Solish et al., 2010 ). While these individuals do gain from extended math education, the cumulative impact of these challenges may prevent them from achieving optimal outcomes comparable to their similarly intelligent TD peers over time. Taken together, our findings emphasize the importance of adopting a developmental, dynamic perspective that considers the complex interplay between intelligence, non-cognitive abilities, and educational environments when examining math abilities in individuals with ASD. Future research and interventions should address these interactions to better support the math development of this population. Method Literature searching and inclusion We searched the following databases for studies investigating the math ability of the ASD population: PsycINFO, MEDLINE, ERIC and PsyARTICLES. In addition, grey literatures have been searched across multiple sources, including conference presentations in Association for Behavior Analysis International (ABAI), International Society for Autism Research (INSAR), Asia Pacific Autism Conference (APAC), Association for Behaviour Analysis Australia (ABA Australia), and Australasian Society for Autism Research (ASFAR), and dissertations in ProQuest. The following keywords were used in the full-text search: (autis*, Asperger) AND (math* OR arithmetic OR number OR numeracy OR algebra OR calcul*). Given that autism was included as a separate diagnostic category in the DSM III in 1980 (APA, 1980), articles published after 1980 were included. The literature search was completed on February 12th, 2024. The search yielded 44,353 articles, and 29,719 were kept after excluding duplicate ones (Fig. 2 ). To be included in the meta-analysis, a study must include participants diagnosed with ASD or autism or Asperger's syndrome; in addition, this study either (a) used standardized math tests and reported scores (including mean and standard deviation) for participants with ASD, regardless of including actual TD control groups, or (b) used non-standardized math tests and reported scores (including mean and standard deviation) for both participants with ASD and the actual TD control group. Studies qualified for (a) allowed to compare the math performance of participants with ASD in the standardized tests with the norm (M = 100, SD = 15). Studies qualified for (b) allowed to compare math performance between the ASD and the TD control groups. Studies qualified for meta-analyses were presented in Table S1 . Three authors reviewed the titles and abstracts of articles, identifying 248 eligible studies. A total of 29471 articles were excluded because a very common word (“number”) was used as a keyword in search. Subsequently, researchers further read the full texts of the 248 articles and identified 78 articles relevant to the purpose of our study. A backward literature search, i.e., searching the references of the eligible articles, resulted in additional 12 articles. The initial pool of 90 articles, which broadly addresses the mathematical abilities of individuals with ASD as detailed in Supplementary Table S1 , was systematically evaluated against specific inclusion criteria for the meta-analysis. Among the 90 articles, three were doctoral dissertations (i.e., Alallawi, 2020; Gwaltney, 2012; Oliver, 2013), one was a master’s dissertation (Howard, 2020), and one doctoral dissertation was replaced by a published article using the same data set (Seh-Bae et al., 2015). Additionally, one was a poster presentation (Brown et al., 2019). This process resulted in a subset of 61 articles included in the quantitative meta-analyses, with their corresponding data presented in Table S1 . Two authors of this article reached 95.08% agreement in literature inclusion (Table S1 ). Three articles were discussed, and the authors agreed to exclude them from the meta-analysis due to no report of math scores (n = 2), or only reporting non-standardized math scores without an actual TD control group (n = 1). Variable coding The following information of studies was recorded (Table S1 ): year of the publication, sample size, age, math tests used in the study, math scores, intelligence tests used in the study, intelligence scores (including the full-scale, verbal and nonverbal IQ, if available), the math-IQ correlation coefficients (if available), ADOS scores, and comorbidity. If a study tested more than one ASD sample (e.g., different age groups), the information for each sample was recorded and coded independently. The above information for both the ASD and TD control groups were recorded, if available. Raw scores of the ADOS assessment were converted to the calibrated severity score (Gotham et al., 2009 ). A particular study may provide only part of these variables. Inquiries for the missing information were emailed to corresponding authors of these studies, but no response was received. Some studies provided more than one type of math scores and thus yielded more than one effect size. To account for the possible dependency among the estimated effect sizes from the same sample, robust variance estimation (RVE) was applied (Hedges et al., 2010 ; Rodgers & Pustejovsky, 2020 ). Data analyses All meta-analyses were conducted using the R package robumeta (Fisher & Tipton, 2015 ), in the following steps. First, we compared the math proficiency of the group with ASD to the norm or the actual TD control groups using random-effects meta-analyses (Fisher & Tipton, 2015 ). Hedges’ g was used as the effect size measure to quantify the standardized group mean difference. Cohen’s d was not used because it tends to overestimate the true effects in small samples (Cooper et al., 2009 , p. 226), which is the case for many samples included in the present meta-analyses. When comparing the group with ASD to the norm, only samples with standardized math tests were included, and the mean and standard deviations of the norm (M = 100; SD = 15) were used to compute the effect sizes. When comparing ASD and TD control groups, the actual means and standard deviations of the two groups’ performance were used to compute effect sizes. Notably, when effect sizes are derived from the same study or share data, dependent effect sizes would result in inaccurate publication bias tests, making it difficult to detect and correct publication bias in this situation (Rodgers & Pustejovsky, 2020 ). Therefore, the results without correction were reported in the text. Second, we used meta-regressions to explore whether the possible group discrepancies in math were moderated by intelligences (including full-scale, verbal and non-verbal IQs), symptom severity, age, publication year, and the interactions between intelligence and age. We further examined if the discrepancies in math performance between the ASD and the TD groups persisted when the IQs of the group with ASD were statistically adjusted to the same level of the TD control groups. To achieve this, we used the standardized mean difference (Hedges’ g) in intelligence as a predictor for the standardized mean difference in math performance. The intercept from this meta-regression represents the expected standardized group difference in math performance when the group difference in intelligence is zero or when the ASD and control groups have equivalent IQ levels. Third, we used the natural logarithm of variability ratio approach developed by Hedges and Nowell ( 1995 ) to compare the variability of mathematical performance (i.e., standardized deviation) between the group with ASD and the norm (SD = 15) or the TD control group (actual SDs). This approach uses the following statistic (i.e., lnVR) as the measure of effect size (i.e., \(\:\text{l}\text{n}\left(\frac{{\sigma\:}_{\text{T}}}{{\sigma\:}_{\text{C}}}\right)\) ): lnVR = \(\:\text{ln}\left(\frac{{s}_{\text{T}}}{{s}_{\text{C}}}\right)+\frac{1}{2({n}_{\text{T}}-1)}-\frac{1}{2({n}_{\text{C}}-1)}\) where \(\:{\sigma\:}_{\text{T}}\) , \(\:{\sigma\:}_{\text{C}}\) \(\:{s}_{1}\) , \(\:{s}_{\text{T}}\) , \(\:{n}_{\text{T}}\) , and \(\:{n}_{\text{C}}\) are the population standard deviations, the sample standard deviations, and the sample sizes of the ASD and control groups, respectively. The sampling variance of the lnVR statistic is computed by \(\:\frac{1}{2({n}_{\text{T}}-1)}+\frac{1}{2({n}_{\text{C}}-1)}\) . A value of zero for the effect size indicates no group discrepancy in variability; a positive value signifies that the variability of the ASD group is greater than that in the control group, while a negative value indicates the opposite. Then, we examined if the possible group discrepancy in variability was moderated by the factors mentioned above. In addition, to further explore the relationship between math ability and intelligence, we examined the correlation and deviation between them (see Supplemental Materials). We calculated the math-IQ Pearson correlation coefficients for ASD and TD control groups respectively. We also calculated the math-IQ deviation by subtracting the standardized math scores from the intelligence scores for the ASD and TD groups, respectively (e.g., Chen et al., 2019 ; Polo‑Blanco et al., 2024), and compared the difference between groups to examine if the math-IQ deviation differed between groups (Morris, 2008 ). References *Aagten-Murphy, D., Attucci, C., Daniel, N., Klaric, E., Burr, D., & Pellicano, E. (2015). Numerical estimation in children with autism. Autism Research, 8 , 668-681. Adams, D., Dargue, N., & Paynter, J. (2023). Longitudinal studies of challenging behaviours in autistic children and adults: A systematic review and meta-analysis. Clinical Psychology Review , 102320. Adreon, D., & Durocher, J. S. (2007). Evaluating the college transition needs of individuals with high-functioning autism spectrum disorders. Intervention in school and clinic , 42 (5), 271-279. Ahmed, W., Minnaert, A., van der Werf, G., & Kuyper, H. (2010). Perceived social support and early adolescents’ achievement: The mediational roles of motivational beliefs and emotions. Journal of youth and adolescence , 39 , 36-46. *Alallawi, B. (2020). Children with autism spectrum disorder in the context of Arab countries and cultures. [PhD thesis, University of Warwick]. University of Warwick Publications service & WRAP. http://webcat.warwick.ac.uk/record=b3467971~S15. American Psychiatric Association (APA). (1980). Diagnostic and statistical manual of mental disorders (3rd ed.) . Washington, DC: Author. American Psychiatric Association (APA). (2013). Diagnostic and statistical manual of mental disorders (5th ed.) . Arlington, VA: Author. Anderson, A. H., Stephenson, J., & Carter, M. (2017). A systematic literature review of the experiences and supports of students with autism spectrum disorder in post-secondary education. Research in Autism Spectrum Disorders , 39 , 33-53. *Assouline, S. G, Nicpon M. F., & Dockery, L. (2012). Predicting the academic achievement of gifted students with autism spectrum disorder. Journal of Autism and Developmental Disorders, 42 (9), 1781-1789. *Bangel, K. A., Batty, M., Ye, A. X., Meaux, E., Taylor, M. J., & Doesburg, S. M. (2014). Reduced beta band connectivity during number estimation in autism. NeuroImage: Clinical, 6 , 202-213. Baron-Cohen, S., Wheelwright, S., Burtenshaw, A., & Hobson, E. (2007). Mathematical talent is linked to autism. Human Nature, 18 , 125-131. Bartfai, A., Nyman, H., & Stegman, B. (1992). WAIS-R manual. Stockholm: Psykologiforlaget AB. *Brown, H. M., Trafford, L., Gaudet, E., Labonté, C., Howard, K., Del Colle, R., et al. (2019, May). Challenging Stereotypes of Math Giftedness and Math Disability in Students with Autism Spectrum Disorder (ASD) [Poster Presentation]. International Society for Autism Research 2019 Palais des congres de Montreal, Montreal, Québec Canada. https://insar.confex.com/insar/2019/webprogram/Paper32162.html Brown, V. L., Cronin, M. E., & McEntire, E. (1994). Test of mathematical abilities (2nd ed., TOMA-2). Austin, TX: Pro- Ed. *Bullen, J. C., Lerro, L. S., Zajic, M., Mcintyre, N., & Mundy, P. (2020). A developmental study of mathematics in children with autism spectrum disorder, symptoms of attention deficit hyperactivity disorder, or typical development. Journal of Autism and Developmental Disorders, 50 (6), 4463-4476. *Bullen, J. C., Zajic, M. C., McIntyre, N., Solari, E., & Mundy, P. (2022). Patterns of math and reading achievement in children and adolescents with autism spectrum disorder. Research in Autism Spectrum Disorders , 92 , 101933. https://doi.org/10.1016/j.rasd.2022.101933. Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54 (1), 1–22. https://doi.org/10.1037/h0046743 Center for Disease Control. (2020b, December 6). Screening and diagnosis of autism spectrum disorder for health care providers. Center for Disease Control. https://www.cdc.gov/ncbddd/autism/hcpscreening.html Charman, T., Jones, C. S., Pickles, A., Simonoff, E., Baird, G., & Happé, F. (2011). Defining the cognitive phenotype of autism. Brain Research , 1380 , 10-21. Chen, L., Abrams, D. M., Rosenberg-Lee, M., Iuculano, T., Wakeman, H. N., Prathap, S., Chen, T., & Menon, V. (2019). Quantitative Analysis of Heterogeneity in Academic Achievement of Children With Autism. Clinical Psychological Science , 7 (2), 362–380. Chiang, H. M., & Lin, Y. H. (2007). Mathematical ability of students with Asperger syndrome and high-functioning autism: A review of literature. Autism, 11, 547-556. Claessens, A., & Engel, M. (2013). How important is where you start? Early mathematics knowledge and later school success. Teachers College Record, 115 (6), 189-210. Connolly, A. J. (2007). KeyMath-3 Diagnostic Assessment Can. Ed. Minneapolis, MN: Pearson. Cooper, H., Hedges, L. V. & Valentine, J. C. (2009). Handbook of research synthesis and meta-analysis (2nd ed). New York: Russell Sage Foundation. Cowan, R., & Frith, C. (2009). Do calendrical savants use calculation to answer date questions? A functional magnetic resonance imaging study. Philosophical Transactions of the Royal Society of London B, 364, 1417-1424. Craig, F., Lamanna, A.L., Margari, F., Matera, E., Simone, M., & Margari, L. (2015). Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder: Searching for Distinctive/Common Clinical Features. Autism Research , 8 , 328-337. de Vos, T. (1992). Tempo Test Rekenen. Amsterdam: Pearson Education. Dekker, R., Dekker, P. H., & Mulder, J. L. (2004). Kaufman Adolescent and Adult Intelligenc Test (KAIT). Leiden, The Netherland: PITS. Diseth, Å., Meland, E., & Breidablik, H. J. (2014). Self-beliefs among students: Grade level and gender differences in self-esteem, self-efficacy and implicit theories of intelligence. Learning and Individual Differences , 35 , 1-8. Duncan, G. J., Dowsett, C. J., & Japel, C. (2007). School readiness and later achievement. Developmental Psychology, 43 , 1428-1466. Eaves, L. C., & Ho, H. H. (1997). School placement and academic achievement in children with autistic spectrum disorders. Journal of Developmental and Physical Disabilities, 9 (4), 277-291. Ehlers, S., Nyden, A., Gillberg, C., Sandberg, A. D., Hjelmqulst, E., & Oden, A. (1997). Asperger syndrome, autism and attention disorders: A comparative study of the cognitive profiles of 120 children. Journal of Child Psychology and Psychiatry, 38 , 207-17. Elliot, C. D. (1990). Differential Ability Scales: Introductory and technical handbook . New York, NY: Psychological Corp. *Estes, A., Rivera, V., Bryan, M., Cali, P., & Dawson, G. (2011). Discrepancies between academic achievement and intellectual ability in higher-functioning school-aged children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 41 (8), 1044-1052. Farag, S. (2011). Stanford-Binet Intelligence Scales 5 th (SBIS-IV) . Cairo: Anglo-Egyptian Bookshop. Fisher, Z., & Tipton, E. (2015). robumeta: An R-package for robust variance estimation in meta-analysis . arXiv preprint arXiv:1503.02220. *Foley-Nicpon, M., Assouline, S. G., & Stinson, R. D. (2012). Cognitive and academic distinctions between gifted student with autism and Asperger syndrome. Gifted Child Quarterly, 52 (2), 77-89. Fombonne, E. (2003). Epidemiological surveys of autism and other pervasive developmental disorders: An update. Journal of Autism and Developmental Disorders , 33 (4), 365–382. Fournier, K. A., Hass, C. J., Naik, S. K., Lodha, N., & Cauraugh, J. H. (2010). Motor coordination in autism spectrum disorders: A synthesis and meta-analysis. Journal of Autism and Developmental Disorders , 40 , 1227-1240 . *Furlano, R., Kelley, E. A., Hall, L., & Wilson, D. E. (2015). Self-perception of competencies in adolescents with autism spectrum disorders. Autism Research, 8 (6), 761-770. Gadke, D. L., McKinney, C., & Oliveros, A. (2016). Autism spectrum disorder symptoms and comorbidity in emerging adults. Child Psychiatry & Human Development , 47 , 194-201. *Gagnon, L., Mottron, L., Bherer, L., & Joanette, Y. (2004). Quantification judgement in high functioning autism: Superior or different? Journal of Autism and Developmental Disorders, 34 (6), 679. Geary, D. C. (2004). Mathematics and learning disabilities. Journal of Learning Disabilities, 37 , 4-15. Geary, D. C. (2011). Cognitive predictors of achievement growth in mathematics: a 5-year longitudinal study. Developmental Psychology, 47 (6), 1539-1552. Gevarter, C., Bryant, D.P., Bryant, B. et al. (2016). Mathematics Interventions for Individuals with Autism Spectrum Disorder: A Systematic Review. Review Journal of Autism and Developmental Disorders , 3 (3), 224–238. Ginsburg, H., & Baroody, A. J. (2003). TEMA-3: Test of early mathematics ability . Austin, TX: Pro-ed. *Goldstein, G., Minshew, N. J., & Siegal, D. J. (1994). Age differences in academic achievement in high-functioning autistic individuals. Journal of Clinical and Experimental Neuropsychology, 16 , 671-680. *Goldstein, G., Beers, S. R., Siegel, D. J., & Minshew, N. J. (2001). A comparison of WAIS-R profiles in adults with high-functioning autism or differing subtypes of learning disability. Applied Neuropsychology, 8 (3), 148-154. Gotham, K., Pickles, A., & Lord, C. (2009). Standardizing ADOS scores for a measure of severity in autism spectrum disorders. Journal of Autism and Developmental Disorders, 39 (5), 693-705. *Griswold, D. E., Barnhill, G. P., Myles, B. S., Hagiwara, T., & Simpson, R. L. (2002). Asperger syndrome and academic achievement. Focus on Autism and Other Developmental Disabilities, 17 (2), 94-102. *Gwaltney, M. V. (2012). Effects of IQ, memory and ADHD symptoms on achievement in students with high functioning autism. University of California, Davis, ProQuest Dissertations Publishing, 2012. 3555314. Happé, F. (1999). Autism: cognitive deficit or cognitive style?. Trends in cognitive sciences , 3 (6), 216-222. Happé, F. (2011). Criteria, categories, and continua: Autism and related disorders in DSM-5. Journal of the American Academy of Child & Adolescent Psychiatry, 50 (6), 540-542. Hedges, L. V., & Friedman, L. (1993). Gender differences in variability in intellectual abilities: A reanalysis of Feingold’s results. Review of Educational Research, 63 (1), 94-105. Hedges, L.V., & Nowell, A. (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. Science, 269 , 41-45. Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta‐regression with dependent effect size estimates. Research synthesis methods , 1 (1), 39-65. *Hetzroni, O., Agada, H., & Leikin, M. (2019). Creativity in autism: An examination of general and mathematical creative thinking among children with autism spectrum disorder and children with typical development. Journal of Autism and Developmental Disorders, 49 (9), 1-12. *Higashionna, T., Iwanaga, R., Tokunaga, A., Nakai, A., Tanaka, K., & Tanaka, G. (2017). The relationship between motor coordination ability, cognitive ability, and academic achievement in Japanese children with autism spectrum disorder and attention deficit/hyperactivity disorder. Brain Science, 12 , 674-684. *Hiniker, A., Rosenberg-Lee, M., & Menon, V. (2016). Distinctive role of symbolic number sense in mediating the mathematical abilities of children with autism. Journal of Autism and Developmental Disorders, 46 , 1268-1281. *Howard, K. (2020). The Role of Achievement Emotions in Math Performance Outcomes of Students with Autism Spectrum Disorder . [Master dissertation, University of Alberta]. University of Alberta Library. https://doi.org/10.7939/r3-aee0-c674. Humphrey, N. (2010). Including pupils with autistic spectrum disorders in mainstream schools. Support for Learning, 23 (1), 41-47. Iuculano, T., Rosenberg-Lee, M., Supekar, K., Lynch, C. J., Khouzam, A., & Phillips, J., Uddin, L. Q., & Menon, V. (2014). Brain organization underlying superior mathematical abilities in children with autism. Biological Psychiatry, 75 (3), 223-230. *Iuculano, T., Padmanabhan A., Chen, L., Nicholas, J., Mitsven, S., & de Los Angeles, C. (2020). Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics. Developmental Cognitive Neuroscience, 42 , 1-12. Jarrold, C., & Russell, J. (1997). Counting abilities in autism: Possible implications for central coherence theory. Journal of Autism and Developmental Disorders, 27 (1), 25-37. Jastak, J. F., & Jastak, S. R. (1965). The Wide Range Achievement Test (WRAT) Manual. Wilmington, DE: Guidance Associates. Jastak, J. E., & Jastak, S. R. (1984). Wide Range Achievement Test Revised (WART-R). Wilmington, DE: Jastak Assoc. *John, T. S., Dawson, G., & Estes, A. (2017). Brief report: Executive function as a predictor of academic achievement in school-aged children with ASD. Journal of Autism and Developmental Disorders, 48 , 276-283. *Jones, C. R., Happé, F., Golden, H., Marsden, A. J., Tregay, J., Simonoff, E., & Charman, T. (2009). Reading and arithmetic in adolescents with autism spectrum disorders: Peaks and dips in attainment. Neuropsychology, 23 (6), 718-728. Kaufman, A. S., & Kaufman, N. L. (1985). Kaufman Test of Educational Achievement (K-TEA) . Circle Pines, MN: American Guidance Service. Kaufman, A. S., & Kaufman, N. L. (2004). Kaufman Brief Intelligence Test, Second Edition (KBIT-2). Bloomington, MN: NCS Pearson Inc. Keen, D., Webster, A., & Ridley, G. (2016). How well are children with autism spectrum disorder doing academically at school? An overview of the literature. Autism, 20 (3), 276-294. *Kim, S. H., Bal, V. H., & Lord, C. (2017). Longitudinal follow-up of academic achievement in children with autism from age 2 to 18. Journal of Child Psychology and Psychiatry, 59 (3), 258-267. *Kim, S. H., Buzzell, G., Faja, S., Choi, Y. B., & Fox, N. (2020). Neural dynamics of executive function in cognitively able kindergarteners with autism spectrum disorders as predictors of concurrent academic achievement. Autism, 24 (3), 1-15. Konstantareas, M. M., & Stewart, K. (2006). Affect regulation and temperament in children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 36 , 143-154. Kurth, J., & Mastergeorge, A. M. (2010). Individual education plan goals and services for adolescents with autism: Impact of age and educational setting. The Journal of Special Education , 44 (3), 146-160. Lewis, A. S., & van Schalkwyk, G. I. (2020). Systematic review: distribution of age and intervention modalities in therapeutic clinical trials for autism spectrum disorder. Journal of autism and developmental disorders , 50 (6), 2208-2216. Lindor, E., Sivaratnam, C., May, T., Stefanac, N., Howells, K., & Rinehart, N. (2019). Problem behavior in autism spectrum disorder: considering core symptom severity and accompanying sleep disturbance. Frontiers in Psychiatry , 10 , 487-497. Maskey, M., Warnell, F., Parr, J.R., Couteur, A. L., & McConachie, H. (2013). Emotional and Behavioural Problems in Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders , 4 3 , 851-859. Mather, N., & Woodcock, R. W. (2001). Examiner's Manual. In Woodcock-Johnson III Tests of Achievement (WJ-III-ACH) . Itasca, IL: Riverside Publishing. Matson, J. L., Cervantes, P. E., & Peters, W. J. (2016). Autism spectrum disorders: management over the lifespan. Expert review of Neurotherapeutics , 16 (11), 1301-1310. *May, T., Rinehart, N., Wilding, J., & Cornish, K., (2013). The role of attention in the academic attainment of children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 43 , 2147-2158. *May, T., Rinehart, N. J., Wilding, J., & Cornish, K. (2015). Attention and basic literacy and numeracy in children with autism spectrum disorder: A one-year follow-up study. Research in Autism Spectrum Disorders, 9, 193-201. *Mayes, S. D., & Calhoun, S. L. (2003a). Ability profiles in children with autism: Influence of age and IQ. Autism, 7 , 65-80. *Mayes, S. D., & Calhoun, S. (2003b). Analysis of WISC–III, Stanford–Binet–IV, and Academic Achievement Test Scores in children with autism. Journal of Autism and Developmental Disorders, 33 , 65-80. *Mayes, S. D., & Calhoun, S. (2006). Frequency of reading, math, and writing disabilities in children with clinical disorders. Learning and Individual Differences, 16 , 145-157. Mayes, S. D., & Calhoun, S. L. (2007). Learning, attention, writing, and processing speed in typical children and children with ADHD, autism, anxiety, depression, and oppositional-defiant disorder. Child Neuropsychology, 13 , 469-493. *Mayes, S. D., & Calhoun, S. L. (2008). WISC-IV and WIAT-II profiles in children with high functioning autism. Journal of Autism and Developmental Disorders, 38 , 428-439. *Mayes, S. D., Waschbusch, D. A., Calhoun, S. L., & Mattison, R. E. (2019). How common are academic overachievement and underachievement in children with autism or ADHD? Journal of Developmental and Physical Disabilities, 32 (4), 775-783. Mayes, S. D., Waschbusch, D. A., Calhoun, S. L., & Mattison, R. E. (2020). Correlates of academic overachievement, nondiscrepant achievement, and learning disability in ADHD, autism, and general population samples. Exceptionality, 28 (1), 1-16. Mazzocco, M. M. M., & Thompson, R. E. (2010). Kindergarten predictors of math learning disability. Learning Disabilities Research, 20 (3), 142-155. *McCauley, J. B., Zajic, M. C., Oswald, T. M., Swain-Lerro, L. E., McIntyre, N. C., & Harris, M. A., et al. (2017). Brief report: Investigating relations between self-concept and performance in reading and math for school-aged children and adolescents with autism spectrum disorder. Journal of Autism and Developmental Disorders , 48 , 1825-1832. *McDougal, E., Riby, D. M., & Hanley, M. (2020). Profiles of academic achievement and attention in children with and without autism spectrum disorder. Research in Developmental Disabilities, 106 , 103749. *Mcknight, M. E., & Culotta, V. P. (2012). Comparing neuropsychological profiles between girls with Asperger’s disorder and girls with learning disabilities. Focus on Autism and Other Developmental Disabilities, 27 (4), 247-253. Meaux, E., Taylor, M. J., Pang, E. W., Vara, A. S., & Batty, M. (2014). Neural Substrates of Numerosity Estimation in Autism . Human Brain Mapping , 35 , 4362-4385. Miller, L. E., Burke, J. D., Troyb, E., Knoch, K, Herlihy, L. E., & Fein, D. A. (2016). Preschool predictors of school-age academic achievement in autism spectrum disorder. Clinical Neuropsychologist, 1744-1765. *Minshew, N. J., Muenz, L. R., Goldstein, G., & Payton, J. B. (1992). Neuropsychological functioning in nonmentally retarded autistic individuals. Journal of Clinical and Experimental Neuropsychology, 14 (5), 749-761. *Minshew, N. J., Goldstein, G., Taylor, H. G., & Siegel, D. J. (1994). Academic achievement in high functioning autistic individuals. Journal of Clinical and Experimental Neuropsychology, 16 (2), 261-270. Morris, S. B. (2008). Estimating Effect Sizes From Pretest-Posttest-Control Group Designs. Organizational Research Methods , 11 (2), 364–386. Mohzan, M. A. M., Hassan, N., & Abd Halil, N. (2013). The influence of emotional intelligence on academic achievement. Procedia-Social and Behavioral Sciences , 90 , 303-312. *Myles, B. S., Simpson, R. L., & Becker, J. (1994). An analysis of characteristics of students diagnosed with higher-functioning autism disorder. Exceptionality, 5 (1), 19-30. Nyrenius, J., & Billstedt, E. (2020). The functional impact of cognition in adults with autism spectrum disorders. Nordic Journal of Psychiatry, 74 (3), 220-225. O'Connor, N., Cowan, R., & Samella, K. (2000). Calendrical calculation and intelligence. Intelligence, 28 (1), 31-48. *Oswald, T. M., Beck, J. S., Iosif, A. M., McCauley, J. B., Gilhooly, L. J., Matter, J., & Solomon, M. (2016). Clinical and cognitive characteristics associated with mathematics problem solving in adolescents with autism spectrum disorder. Autism Research, 9 , 480-490. Pellicano, E. (2012). The development of executive function in autism. Autism research and treatment , 2012 (1), 146132. Peng, P., Fei, W. T., Cui, W. C., & Lin, X. (2019). A meta-analysis on the relation between fluid intelligence and reading/mathematics: Effects of tasks, age, and social economics status. Psychological Bulletin, 45 (2), 189-236. Pennington, B. F., & Ozonoff, S. (1996). Executive functions and developmental psychopathology. Journal of Child Psychology and Psychiatry, 37 (1), 51-87. Philip, R. C., Dauvermann, M. R., Whalley, H. C., Baynham, K., Lawrie, S. M., & Stanfield, A. C. (2012). A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neuroscience & Biobehavioral Reviews , 36 (2), 901-942. Postlethwaite, B. E. (2011). Fluid ability, crystallized ability, and performance across multiple domains. [Doctoral thesis, University of Iowa]. University of Iowa. https://doi.org/10.17077/etd.zopi8wvs Raven, J., Raven, J. C., & Court, J. H. (1998). Manual for Raven’s Progressive Matrices and Vocabulary Scales. San Antonio: Harcourt Assessment. Raven, J., Raven, J. C., & Court, J. H. (2008). Raven’s Educational: Coloured Progressive Matrices (CPM) and Crichton Vocabulary Scale Manual. London: NCS Pearson. Roberts, J., & Simpson, K. (2016). A review of research into stakeholder perspectives on inclusion of students with autism in mainstream schools. International Journal of Inclusive Education , 20 (10), 1084-1096. Rodgers, M. A., & Pustejovsky, J. E. (2020). Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes. Psychological methods , 26 (2), 141-160. Root, J. R., Ingelin, B., & Cox, S. K. (2021). Teaching mathematical word problem solving to students with autism spectrum disorder: A best-evidence synthesis. Education and Training in Autism and Developmental Disabilities , 56 (4), 420-436. Rosenfeld, L. B., Richman, J. M., & Bowen, G. L. (2000). Social support networks and school outcomes: The centrality of the teacher. Child and adolescent social work journal , 17 , 205-226. *Rumsey, J. M., & Hamburger, S. D. (1988). Neuropsychological findings in high-functioning men with infantile autism, residual state. Journal of Clinical and Experimental Neuropsychology, 10 (2), 201-221. *Rumsey, J. M., & Hamburger, S. D. (1988). Neuropsychological divergence of high-level autism and severe dyslexia. Journal of Autism and Developmental Disorders, 20 , 155-168. Rust, J., Golombok, S., & Trickey, G. (1993). Wechsler Objective Numerical Dimensions (WOND) Manual. London: The Psychological Corporation. Sacks, O. (1995). The man who mistook his wife for a hat. British Journal of Psychiatry, 166 , 130-131. Schneider, W., & Niklas, F. (2017). Intelligence and verbal short-term memory/working memory: Their interrelationships from childhood to young adulthood and their impact on academic achievement. Journal of Intelligence , 5 (2), 26. *Seh-Bae, Y., Chiang, H. M., & Hickson, L. (2015). Mathematical word problem solving ability of children with autism spectrum disorder and their typically developing peers. Journal of Autism and Developmental Disorders, 45 (7), 2200-2208. Solish, A., Perry, A., & Minnes, P. (2010). Participation of children with and without disabilities in social, recreational and leisure activities. Journal of Applied Research in Intellectual Disabilities, 23 , 226-236. Soulières, I., Hubert, B., Rouleauc, N., Gagnone, L., Tremblay, P., Seron, X., & Mottron, L. (2010). Superior estimation abilities in two autistic spectrum children. Cognitive Neuropsychiatry, 27 , 261-276. *Stroizer, S., Hinton, V., Flores, M., & Terry, L. (2015). An investigation of the effects of CRA instruction and students with autism spectrum disorder. Education and Training in Autism and Developmental Disabilities, 50 (2), 223-236. Szatmari, P., Georgiades, S., Duku, E., Bennett, T. A., Bryson, S., Fombonne, E., Mirenda, P., Roberts, W., Smith, I., M., Vaillancourt, T., Volden, J., Waddell, C., Zwaigenbaum, L., Elsabbagh, M., & Thompson, A. (2015). Developmental trajectories of symptom severity and adaptive functioning in an inception cohort of preschool children with autism spectrum disorder. JAMA Psychiatry , 72 (3), 276–283. The Psychological Corp. (1992). Wechsler Individual Achievement Test (WIAT). New York. Titeca, D., Roeyers, H., Josephy, H., Ceulemans, A., & Desoete, A. (2014). Preschool predictors of mathematics in first grade children with autism spectrum disorder. Research in Developmental Disabilities, 35 (11), 2714-2727. Titeca, D., Roeyers, H., & Desoete, A. (2015a). Early numerical competencies in 4- and 5- year- old children with autism spectrum disorder. Focus on Autism and Other Developmental Disabilities, 32 (4), 279-292. *Titeca, D., Roeyers, H., & Desoete, A. (2015b). Early numerical competencies in 5- and 6-year-old children with autism spectrum disorder. Early Education and Development, 26 (7), 1012-1034. Tonizzi, I., & Usai, M. C. (2023). Math abilities in autism spectrum disorder: A meta-analysis. Research in Developmental Disabilities , 139 , 104559. Tonizzi, I., & Usai, M. C. (2024). Cognitive correlates of math abilities in autism spectrum disorder. PLoS ONE , 19(9), e0310525. *Tops, W., Bergh, A. V. D., Noens, I., & Baeyens, D. (2017). A multi-method assessment of study strategies in higher education students with an autism spectrum disorder. Learning and Individual Differences, 59 , 141-148. *Troyb, E., Orinstein, A., Tyson, K., Helt, M., Eigsti, I. M., Stevens, M., & Fein, D. (2014). Academic abilities in children and adolescents with a history of autism spectrum disorders who have achieved optimal outcomes. Autism, 18 (3), 233-243. Turi, M., Burr, D. C., Igliozzi, R., Aagten-Murphy, D., Muratori, F., & Pellicano, E., et al. (2015). Children with autism spectrum disorder show reduced adaptation to number. Proceedings of the National Academy of Sciences of the United States of America, 112 (25), 7868-7872. Wallace, G. L., Dudley, K., Anthony, L., Pugliese, C. E., Orionzi, B., Clasen, L., & Clasen, L., et al. (2017). Divergence of age-related differences in social-communication: Improvements for typically developing youth but declines for youth with autism spectrum disorder. Journal of autism and developmental disorders , 47 , 472-479. Wang, Q., Han, Z., Hu, X., Feng, S., Wang, H., Liu, T., & Yi, L. (2020). Autism symptoms modulate interpersonal neural synchronization in children with autism spectrum disorder in cooperative interactions. Brain Topography , 33 , 112-122. *Wang, L., Liang, X., Jiang, B., Wu, Q., & Jiang, L. (2022). What ability can predict mathematics performance in typically developing preschoolers and those with autism spectrum disorder? Journal of Autism and Developmental Disorders . https://doi.org/10.1007/s10803-022-05454-w Wang, X. S., Perry, L. B., Malpique, A., & Ide, T. (2023). Factors predicting mathematics achievement in PISA: a systematic review. Large-Scale Assessments in Education , 11 (1), 24. Wechsler, D. (1949). Wechsler Intelligence Scale for Children (WISC). New York: The Psychological Corporation. Wechsler, D. (1955). Wechsler Adult Intelligence Scale (WAIS) Manual. New York: Psychological Corporation. Wechsler, D. (1974). Wechsler Intelligence Scale for Children-Revised (WISC-R). New York: The Psychological Corporation. Wechsler, D. (1981). Wechsler Adult Intelligence Scale-Revised (WAIS-R). New York: The Psychological Corporation. Wechsler, D. (1991). Wechsler Intelligence Scale for Children - Third Edition Manual (WISC-III). New York: Psychological Corporation. Wechsler, D. (1997). Wechsler Adult Intelligence Scale (3rd ed.) (WAIS-III) . San Antonio, TX: The Psychological Corporation. Wechsler, D. (1999). Wechsler Abbreviated Intelligence Scale (WAIS). San Antonio, TX: Pearson Publishers. Wechsler, D. (1999). The Wechsler Abbreviated Scale of Intelligence-UK (WASI). San Antonio, TX: The Psychological Corporation. Wechsler, D. (2002). Wechsler individual achievement test II. San Antonio, TX: Psychological Corporation. Wechsler, D. (2003). WISC-IV technical and interpretive manual. San Antonio, TX: Psychological Corporation. Wechsler, D. (2004). Wechsler Preschool and Primary School Intelligence III (WPPSI-III) Australian: Administration and scoring manual. NSW: Harcourt Assessment. Wechsler, D. (2009). Wechsler Individual Achievement Scales, III (WIAS-III). San Antonio, TX: The Pearson Publishers. Wechsler, D. (2011). Wechsler Abbreviated Scale of Intelligence Second Edition . San Antonio, TX: Pearson. Wei, X., Blackorby, J., & Schiller, E. (2011). Growth in reading achievement in a national sample of students with disabilities ages 7 to 17. Exceptional Children, 78 , 89-106. Wei, X., Yu, J. W., Shattuck, P., McCracken, M., & Blackorby, J. (2012). Science, technology, engineering, and mathematics (STEM) participation among college students with an autism spectrum disorder. Journal of Autism and Developmental Disorders, 43 , 1-8. Wei, X., Lenz, K. B., & Blackorby, J. (2013). Math growth trajectories of students with disabilities: Disability category, gender, racial, and socioeconomic status differences from ages 7 to 17. Remedial and Special Education, 34 (3), 154-165. Wei, X., Elizabeth, R. A., Jennifer, W., Mary, W., & Spiker, D. (2014). Reading and math achievement profiles and longitudinal growth trajectories of children with an autism spectrum disorder. Autism, 19 (2), 200-210. *Williams, D. L., Goldstein, G., & Minshew, N. J. (2006). The profile of memory function in children with autism. Neuropsychology, 20 (1), 21-29. Woodcock, R. W., & Johnson, M. B. (1977). Woodcock- Johnson Psycho-Educational Battery. Allen, TX: DLM Teaching Resources. Yakubova, G., Hughes, E. H., & Baer, B. L. (2020). Supporting students with ASD in mathematics learning using video-based concrete-representational abstract sequencing instruction. Preventing School Failure: Alternative Education for Children and Youth , 64 (1), 12-18. *Zaidman-Zait, A., Mirenda, P., Szatmari, P., Duku, E., & Elsabbagh, M., et al. (2020). Profiles and predictors of academic and social school functioning among children with autism spectrum disorder. Journal of Clinical Child and Adolescent Psychology, 50 (5), 656-668. Zheng, S., Hume, K., Able, H., Bishop, S. L., & Boyd, B. A. (2020). Exploring Developmental and Behavioral Heterogeneity among Preschoolers with ASD: A Cluster Analysis on Principal Components. Autism Research , 13 (5), 796-809. Tables Table 1 Meta-analysis results Estimates B SE 95% CI k P ASD vs. Norm Math proficiency -.327 .119 [-.565, -.089] 87 .008 Moderators ADOS -.501 .446 [-1.607, .605] 19 .306 Age .015 .020 [-.030, .609] 78 .465 Publication year .001 .010 [-.020, .021] 87 .951 Full-scale IQ of ASD .049 .007 [.032, .066] 72 <.001 Verbal IQ of ASD .048 .005 [.036, .060] 53 <.001 Non-verbal IQ of ASD .053 .002 [.047, .059] 54 <.001 Interaction effect Full-scale IQ * Age -.108 .168 [-.580, .364] 66 .558 Verbal IQ * Age .034 .145 [-.360, .429] 47 .823 Non-verbal * Age .003 .060 [-.144, .151] 48 .959 Math variability .170 .029 [.112, .228] 87 <.001 Moderators ADOS -.086 .049 [-.209, .037] 19 .134 Age .018 .005 [.006, .030] 78 .008 Publication year -.001 .003 [-.007, .005] 87 .772 Full-scale IQ of ASD -.004 .002 [-.008, -.0002] 72 .043 Verbal IQ of ASD -.001 .003 [-.008, .005] 53 .627 Non-verbal IQ of ASD .001 .003 [-.006, .008] 54 .747 Interaction effect Full-scale IQ * Age .013 .037 [-.095, .121] 66 .745 Verbal IQ * Age -.238 .083 [-.445, -.031] 47 .031 Non-verbal * Age -.114 .068 [-.278, .051] 48 .143 ASD vs. TD Math proficiency -.676 .141 [-.962, -.390] 66 <.001 Moderators ADOS -.030 .292 [-.948, .887] 16 .924 Age .019 .014 [-.011, .050] 62 .187 Publication year -.031 .011 [-.055, -.006] 66 .019 Full-scale IQ of ASD .052 .013 [.022, .082] 47 .003 Full-scale IQ of TD .-.037 .009 [-.059, -.014] 46 .006 Verbal IQ of ASD .054 .016 [.013, .094] 44 .019 Verbal IQ of TD -.023 .011 [-.047, -002] 44 .064 Non-verbal IQ of ASD .045 .013 [.013, .077] 43 .012 Non-verbal IQ of TD -.054 .012 [-.082, -.027] 43 .002 Interaction effect Full-scale IQ * Age -.159 .044 [-.274, -.045] 48 .016 Verbal IQ * Age -.086 .330 [-.916, .744] 44 .805 Non-verbal * Age -.319 .242 [-.918, .280] 43 .238 Math variability .312 .052 [.207, .417] 66 <.001 Moderators ADOS .206 .1210 [-.175, .586] 16 .184 Age -.002 .010 [-.023, .020] 62 .869 Publication year .006 .005 [-.004, .017] 51 .224 Full-scale IQ of ASD .003 .008 [-.016, .022] 48 .738 Full-scale IQ of TD -.0001 .010 [-.024, .024] 46 992 Verbal IQ of ASD -.003 .004 [-.014, .007] 44 .471 Verbal IQ of TD .008 .004 [-.002, .017] 44 .098 Non-verbal IQ of ASD -.004 .005 [-.016, .007] 43 .389 Non-verbal IQ of TD -.004 .005 [-.010, .023] 43 .368 Interaction effect Full-scale IQ * Age .110 .059 [-.042, .262] 48 .122 Verbal IQ * Age .059 .101 [-.183, .301] 44 .578 Non-verbal * Age -.003 .116 [-.281, .274] 43 .978 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementalMaterials.docx Supplemental Materials of The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis Rcode.zip R code Cite Share Download PDF Status: Published Journal Publication published 18 Feb, 2026 Read the published version in Nature Human Behaviour → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5667808","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":397297120,"identity":"b60ebfd4-b1e2-45bd-a753-69c1ab501116","order_by":0,"name":"Yi Mou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYLCCxD82DAzMzA0wvgERWhrSgFoYSdHC2HAYTBKnRbe9x0zi4Y7z0fztjA2MP9vqEhvYm7dJMNTcwanF7MwZM4nEM7dzZxxmbGDmbTuc2MBzrEyC4dgz3Fpu5JhJJLDdzm0AaWFsO5DYIAEUgTgVr5ZzufMPwxwm/4YILYltB3I3ALUw8LYxA23hIaDlzLFii4QzybkbgVoO85w7bNzGkwYUOYZHy/HmjTd/VNjlzjt/+ODDH2V1sv3shzfe+FCDWwsQsEjAWAcY2RgY2ECsBHwagNH+AcH+g1/pKBgFo2AUjEwAANeSWfbwTIoiAAAAAElFTkSuQmCC","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Mou","suffix":""},{"id":397297121,"identity":"a137515e-7f30-4798-a110-ef537881a1c6","order_by":1,"name":"Jiaxi Li","email":"","orcid":"https://orcid.org/0000-0001-8697-4049","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Jiaxi","middleName":"","lastName":"Li","suffix":""},{"id":397297122,"identity":"7f2af4cc-69c6-4c35-a26c-a139b6a1b923","order_by":2,"name":"Zijun Ke","email":"","orcid":"","institution":"Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health, and Department of Psychology, Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Zijun","middleName":"","lastName":"Ke","suffix":""},{"id":397297123,"identity":"a4feb934-f680-442a-a027-6107b060c7ff","order_by":3,"name":"Xueyan Li","email":"","orcid":"","institution":"Chongqing University of Education","correspondingAuthor":false,"prefix":"","firstName":"Xueyan","middleName":"","lastName":"Li","suffix":""},{"id":397297124,"identity":"6421fdc0-4f3f-4d5f-ad1f-834a8dc83b8b","order_by":4,"name":"Bo Zhang","email":"","orcid":"","institution":"University of Illinois Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhang","suffix":""},{"id":397297125,"identity":"00d46c02-c147-49d7-8bc8-17d5152aa78c","order_by":5,"name":"Yini Liao","email":"","orcid":"","institution":"Sun Yat-sen University, Department of Psychology","correspondingAuthor":false,"prefix":"","firstName":"Yini","middleName":"","lastName":"Liao","suffix":""}],"badges":[],"createdAt":"2024-12-18 09:01:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5667808/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5667808/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41562-025-02384-2","type":"published","date":"2026-02-18T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73084524,"identity":"c5579ee5-d09e-4d58-b4a8-11efdc8614b2","added_by":"auto","created_at":"2025-01-06 14:40:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure. 2 \u003c/strong\u003e\u003cem\u003eThe PRISMA Chart for Article Inclusion.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5667808/v1/176fa595c524a87310b02f8e.png"},{"id":102977091,"identity":"81ca406a-b3b1-4800-8f69-308293fbc035","added_by":"auto","created_at":"2026-02-19 08:06:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1182783,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5667808/v1/2155f012-cc2f-44dd-932f-f10e5aaeb817.pdf"},{"id":73085049,"identity":"1eac94e2-3724-4030-9925-a69c27af0bbd","added_by":"auto","created_at":"2025-01-06 14:48:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":59940,"visible":true,"origin":"","legend":"Supplemental Materials of The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis","description":"","filename":"SupplementalMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5667808/v1/6b8cac81b8c096f483f19945.docx"},{"id":73084529,"identity":"f650771d-cb11-4225-8fd8-f562139256f8","added_by":"auto","created_at":"2025-01-06 14:40:14","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":308513,"visible":true,"origin":"","legend":"R code","description":"","filename":"Rcode.zip","url":"https://assets-eu.researchsquare.com/files/rs-5667808/v1/031f6c125ecc182e91685ed1.zip"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAutism spectrum disorder (ASD) is a neurodevelopmental disorder, characterized by major deficits in social communication and interaction, and/or repetitive or ritualized behaviors (APA, 2013). The growing population with ASD needs specialized educational support to foster academic achievement (Bullen et al., 2020; Humphrey, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Mathematics is an essential subject closely related to academic achievement and life success (Claessens \u0026amp; Engel, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Duncan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), making it important for understanding the academic profiles of ASD. Despite this importance, the math abilities of individuals with ASD remain understudied (Mayes et al., 2019; Keen et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Two fundamental aspects of mathematics ability in this population - proficiency (strength) and variability (heterogeneity) - have not been fully characterized. It remains unclear whether individuals with ASD exhibit comparable math proficiency and variability to the general population (Keen et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and what factors may contribute to these group discrepancies, if there are. This knowledge gap hinders the development of a robust theoretical framework for the math abilities of ASD. For instance, while some theories suggest that individuals with ASD may exhibit strengths in math (Baron-Cohen, 2007), others posit that they may face challenges (e.g., cognitive dysfunction theories; Happ\u0026eacute;, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Existing research needs to be synthesized to resolve these conflicting perspectives and provide a foundation for understanding their math abilities. Furthermore, clarifying these fundamental characteristics is essential for informing targeted educational interventions aimed at optimizing academic outcomes for the ASD population.\u003c/p\u003e\n\u003ch3\u003eProficiency and variability in math ability\u003c/h3\u003e\n\u003cp\u003ePrevious studies have reported highly inconsistent findings on the math proficiency of individuals with ASD. Some studies find that individuals with ASD, ranging from preschoolers to adults, presented comparable scores to TD control groups in standardized math tests (e.g., Mayes \u0026amp; Calhoun 2008; Tops et al., 2017) and specific mathematics tasks, such as non-symbolic numerical comparison, verbal numerical estimation, counting, and symbolic arithmetic (e.g., Titeca et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Turi et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A few studies have even highlighted their superior math abilities in calendar calculation (O'Connor et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), number estimation and decomposition (Sacks, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Souli\u0026egrave;res et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and computation (Cowan \u0026amp; Frith, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Souli\u0026egrave;res et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, individuals with ASD also exhibit lower scores on standardized math tests (e.g., Bullen et al., 2020; Wei et al., \u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and specific math tasks such as counting (Jarrold \u0026amp; Russell, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), verbal number estimation (Meaux et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and non-symbolic and symbolic number representation (Aagten-Murphy et al., 2015; Li et al., 2023). Additionally, a higher proportion of individuals with ASD had learning disabilities in math (e.g., 22% in Oswald et al., 2016) compared to the general population (e.g., 5\u0026ndash;10%; Geary, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mazzocco \u0026amp; Thompson, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding the variability, or heterogeneity, in math abilities among individuals with ASD is equally crucial because it can enhance insight into individual differences of their cognitive profiles and informs the development of individualized educational strategies (Wang et al., \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yakubova et al., \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some studies report that individuals with ASD exhibit larger or smaller standard deviations in math scores, compared to TD groups (Aagten-Murphy et al., 2015; Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gagnon et al., 2004; Hiniker et al., 2016), but most of them simply presented the variabilities without statistically comparing them between groups, leaving an open question about if individuals with ASD indeed exhibit greater math variability than the general population.\u003c/p\u003e \u003cp\u003eTo date, two meta-analyses have examined the math abilities of individuals with ASD reporting that this population scores below the normative average on standardized tests (M\u003csub\u003enorm\u003c/sub\u003e = 100, Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.2; Chiang \u0026amp; Lin, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) or their TD peers (Hedges\u0026rsquo; g\u0026thinsp;=\u0026thinsp;0.49; Tonizzi \u0026amp; Usai, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While these studies provide valuable insights, they have several limitations. First, the scope of these meta-analyses was limited in sample size and temporal range (e.g., including only eight empirical studies published before 2004, or 13 studies published between 2013 and 2020), hindering a comprehensive understanding of the state of research in this field and potential changes in math abilities among individuals with ASD over the past several decades. Second, they focused on math proficiency without accounting for variability within the ASD population. Third, they compared the ASD groups\u0026rsquo; math scores to either normative averages or actual TD control groups, rather than both. Comparisons to normative averages provide a baseline for understanding where individuals with ASD stand relative to the norm, and comparisons to TD control groups, on the other hand, control for more confounding variables such as age, intelligence, and demographic factors, allowing for a clearer attribution of math performance discrepancies to autism-specific factors rather than unrelated variables.\u003c/p\u003e \u003cp\u003eFourth, although one meta-analysis (Tonizzi \u0026amp; Usai, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) explored the influence of certain cognitive (e.g., intelligence) and demographic factors (e.g., age) on group differences in math ability, these analyses were constrained by limited distributions (e.g., age range: 9.39\u0026ndash;14.88 years). This restricted range precluded a more nuanced examination of how these factors interact to influence math performance. Furthermore, important non-cognitive factors, such as ASD symptom severity, were not considered despite evidence suggesting their relevance (Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oswald et al., 2016). Addressing these limitations by examining multiple cognitive and non-cognitive moderators across broader samples and timeframes is critical.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eModerators\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eIntelligence\u003c/h2\u003e \u003cp\u003eOne key factor in understanding math ability is the relationship between math and intelligence, as this sheds light on how general cognitive abilities contribute to and interact with math development (Peng et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Geary, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A moderate math-IQ relationship is typically observed in the general population, although the strength of the correlation varies depending on the component of intelligence (Peng et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Postlethwaite, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These components include verbal intelligence (or crystallized intelligence), non-verbal intelligence (or fluid intelligence), and general intelligence (a composite of both verbal and non-verbal abilities; Cattell, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1963\u003c/span\u003e; Wechsler, \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Woodcock-Johnson, 1977).\u003c/p\u003e \u003cp\u003eIn the ASD population, however, the math-IQ relationship remains unclear. While some studies found the positive math-IQ link in the ASD population, even after controlling for factors such as ASD severity, adaptive behaviors, or test anxiety, other studies found no significant correlations (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Additionally, several studies have highlighted their deviation, with some individuals exhibiting either superior or weaker math proficiency despite having average intelligence, resulting in substantial differences between math and IQ scores (Estes et al., 2011; Jones et al., 2009; Kim et al., 2017). These findings suggest that their math abilities may not be reliably predicted by IQs.\u003c/p\u003e \u003cp\u003eThe math-IQ relationship in ASD can be investigated through various approaches. One method involves pooling the reported math-IQ correlation coefficients, as well as assessing the math-IQ deviation (i.e., the difference between math and IQ scores) across studies to determine the overall relationship. Another approach is to examine whether intelligence moderates potential discrepancies in math proficiency between individuals with ASD and the general population. Specifically, if a positive math-IQ relationship exists in the ASD population, we would expect math scores - relative to the normative average - to increase as intelligence rises.\u003c/p\u003e \u003cp\u003eAdditionally, it is critical to explore whether intelligence moderates the potential math proficiency discrepancy between individuals with ASD and TD groups, extending the analysis beyond normative averages to control for participants with a range of math abilities. This also allows to examine whether the math proficiency discrepancy between ASD and TD groups differs at various levels of intelligence. Previous research has shown that individuals with ASD who exhibit higher verbal intelligence demonstrate smaller discrepancies in math proficiency relative to their TD peers (Tonizzi \u0026amp; Usai, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the limited range of intelligence scores (96\u0026ndash;117) in this analysis raises the question of whether this moderating effect persists across a broader spectrum of intelligence levels.\u003c/p\u003e \u003cp\u003eFurthermore, neither this research nor any others have explored whether intelligence moderates the variability in math abilities between individuals with ASD and the general population. Greater math variability in ASD groups may persist across different intelligence levels, or increases as intelligence rises, potentially due to reduced cognitive constraints in these individuals, allowing non-intelligence-related factors to exert a stronger influence (Chen et al., 2018; Tonizzi \u0026amp; Usai, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Alternatively, the group discrepancy in variability may reduce with increasing intelligence, as high intelligence may enable individuals with ASD to mitigate cognitive challenges, such as difficulties with executive functioning (Pennington \u0026amp; Ozonoff, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), resulting in more consistent and predictable pattern of math performance. These hypotheses warrant empirical investigation, and examining whether and how math variability in individuals with ASD is related to intelligence could provide indispensable insights into the math-IQ relationship within this population.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eAge\u003c/h3\u003e\n\u003cp\u003eAs the challenges of learning math typically increase with age, it is necessary to explore whether math abilities of individuals with ASD change as they grow, compared to those of the general population. Previous findings are inconsistent: compared to TD peers, individuals with ASD show either comparable growth in math proficiency with age (Hiniker et al., 2016; Kim et al., 2017), or a slower increase that results in enlarged group discrepancies during development (Wang et al., 2022; Wei et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A study found that school-aged individuals with ASD (9\u0026ndash;15 years) exhibit a larger discrepancy in math proficiency compared to their TD peers as they age (Tonizzi \u0026amp; Usai, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, it remains unclear whether this trend persists across a broader age range, including preschool to adulthood. If the risk of falling further behind TD peers continues throughout the lifespan, this underscores the importance of providing early and continuous support for these individuals.\u003c/p\u003e \u003cp\u003eEven less is known about whether math variability between groups varies with age, as no studies have quantitatively examined whether the group discrepancy in math variability (e.g., standard deviations of math performance) between individuals with ASD and the general population changes across different age groups. Given that variability in certain cognitive abilities and other developmental domains, such as executive functions (Pellicano, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and social communication skills (Wallace et al., \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), tends to broaden with age in individuals with ASD, it is plausible that similar trends may emerge in their math abilities (Charman et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As a result, the discrepancy in math variability between individuals with ASD and the general population may widen over time. However, this hypothesis remains to be empirically tested.\u003c/p\u003e \u003cp\u003eWhile a previous study explored the moderating effects of age and intelligence on the group discrepancy in math separately (Tonizzi \u0026amp; Usai, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), their interaction has not been examined. Investigating this interaction could reveal whether individuals with ASD at different intelligence levels exhibit distinct developmental trajectories in math ability over time. Previous research descriptively indicates that ASD children with higher intelligence tend to exhibit better math proficiency and less variability than those with lower intelligence, with these distinctions becoming even more marked from childhood to adolescence (Kim et al., 2017). Quantifying the interactive effects of intelligence and age on math performance would provide a more developmental perspective on the role of cognitive factors in math learning for individuals with ASD.\u003c/p\u003e\n\u003ch3\u003eSymptom Severity\u003c/h3\u003e\n\u003cp\u003eIndividuals with ASD present a high degree of heterogeneity in the severity of symptoms (APA, 2013). More severe symptoms are often associated with weaker social and cognitive competence and an increase in problem behaviors (APA, 2013), which may collectively contribute to more difficulties in academic learning, including mathematics. However, while some studies found a significant correlation between ASD symptom severity, as measured by tools like the Autism Diagnostic Observation Schedule (ADOS; Center for Disease Control, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), and math proficiency, others have not (e.g., Miller et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zaidman-Zait et al., 2020), highlighting the need for meta-analyses to further explore these relationships.\u003c/p\u003e \u003cp\u003eMoreover, autism severity may also be associated with math variability, as more severe symptoms are often accompanied by a higher prevalence of comorbid conditions (Gadke et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and increased problem behaviors (Lindor et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), differentially impacting math learning and resulting in greater variability. Conversely, severe autism symptoms might correspond to more substantial cognitive and non-cognitive challenges and the need for interventions focused on primary life or social skills (Gadke et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lindor et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These may limit opportunities to engage in math learning and lead to reduced variability in math abilities. These hypotheses remain speculative and require further investigation.\u003c/p\u003e\n\u003ch3\u003ePublication Year\u003c/h3\u003e\n\u003cp\u003eThe evolution of autism as a diagnostic category since its formal recognition in the DSM III has been marked by significant changes in diagnostic criteria, intervention strategies, public perception, and social support systems (APA, 1980; 2013). These alterations have broadened the autism spectrum, potentially impacting the academic performance and heterogeneity of individuals with ASD over time (Charman et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, more individuals with ASD are receiving inclusive education, and targeted interventions specific to math have also increased in recent years (Gevarter et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Roberts \u0026amp; Simpson, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Root et al. \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which may have a sustained positive impact on their math performance.\u003c/p\u003e \u003cp\u003eHowever, there may be little substantial change in their math ability over past decades, as math ability is not a core diagnostic criterion or a primary target of interventions for most individuals with ASD. Notably, this oversight may disadvantage them and exacerbate the discrepancies in math abilities compared to the general population, particularly as math skills have become increasingly emphasized and training has been enhanced for the general population in recent years (Wang et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, it remains unknown whether and how the math proficiency and variability of individuals with ASD have changed over time.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe current study\u003c/h2\u003e \u003cp\u003eThis meta-analysis was conducted to characterize two fundamental features of math ability\u0026mdash;proficiency and variability\u0026mdash;of individuals with ASD, and to compare these features with those of the general population. Previous studies used various math assessments, including standardized math tests (e.g., Woodcock-Johnson III Test of Achievement, WJ-III-ACH, Mather \u0026amp; Woodcock, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and non-standardized tasks measuring specific math aspects. To ensure a comprehensive analysis, all these types of math assessments were included in this meta-analysis. We compared individuals with ASD to the general population, represented by both the norms of standardized math tests (typically normed by age) and actual TD control groups that often matched the ASD group in multiple factors (e.g., age, intelligence, or socioeconomic factors).\u003c/p\u003e \u003cp\u003eFurthermore, we examined whether the potential group discrepancies in math was moderated by factors including intelligence, age, ASD symptom severity, and publication year. Moreover, given the potential interaction between intelligence and age on the development of math abilities in individuals with ASD (Kim et al., 2017; Wei et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), we also examined this interaction in meta-analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGroup discrepancies in math proficiency between ASD groups and norms\u003c/h2\u003e \u003cp\u003eEighty-seven samples from 47 studies reported standardized math test scores, involving 3051 participants with ASD. The sample sizes of these studies ranged from 3 (Stroizer et al., 2015) to 164 (Mayes et al., 2019). The groups with ASD exhibited lower math scores (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003emean\u003c/em\u003e\u003c/sub\u003e = 95.97, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003emean\u003c/em\u003e\u003c/sub\u003e = 16.87; \u003cem\u003eRange\u003c/em\u003e\u003csub\u003e\u003cem\u003emean\u003c/em\u003e\u003c/sub\u003e = 53.30-138.31) compared to the normative average of standardized math tests (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100; \u003cem\u003eHedges\u0026rsquo; g\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.327, 95%CI = [-.565, \u0026minus;\u0026thinsp;.089], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008, k\u0026thinsp;=\u0026thinsp;87). Their average IQs were 99.47, 97.59, and 99.44 for full-scale, verbal, and non-verbal IQs, respectively. Meta-regressions revealed that the group discrepancy was moderated by three types of IQs (full-scale IQ: \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.049, 95%CI = [.032, .066], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; verbal IQ: \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.048, 95%CI = [.036, .060], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; non-verbal IQ: \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.053, 95%CI = [.047, .059], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that the difference in math scores between the ASD group and the norm decreased as the IQs of the ASD group increased. None of the other moderators or interactions showed significant effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGroup discrepancies in math variability between ASD groups and norms\u003c/h2\u003e \u003cp\u003eThe standard deviation of math scores of groups with ASD (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 17.75, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 4.79; \u003cem\u003eRange\u003c/em\u003e\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 4.04\u0026ndash;32.26) was 1.185\u0026thinsp;=\u0026thinsp;\u003cem\u003eexp\u003c/em\u003e(0.170) times greater than that of the norm (\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15; \u003cem\u003elnVR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.170, 95% CI = [.112, .228], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, k\u0026thinsp;=\u0026thinsp;87). This group discrepancy was moderated by full-scale IQ and age of the ASD group (full-scale IQ: \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.004, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.043; age: \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.018, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.008), suggesting that the group discrepancy decreased when the ASD group had the higher IQ, and increased with age. There was no significant effect of other moderators.\u003c/p\u003e \u003cp\u003eThere is also a significant interaction effect between verbal IQ and age (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.238, 95%CI = [-.445, \u0026minus;\u0026thinsp;.031], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.031, k\u0026thinsp;=\u0026thinsp;47). Verbal IQ scores ranged from 63.00 to 117.31, with an average of 99.22. For the ASD group with verbal IQ scores below the mean, the slope of age was 0.249 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, simple intercept\u0026thinsp;=\u0026thinsp;0.231, age\u0026thinsp;=\u0026thinsp;4.50-26.62 years; k\u0026thinsp;=\u0026thinsp;25); for the ASD group with verbal IQ scores above the mean, the age slope was \u0026minus;\u0026thinsp;0.227 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, simple intercept\u0026thinsp;=\u0026thinsp;0.095, age\u0026thinsp;=\u0026thinsp;8.27\u0026ndash;26.40; k\u0026thinsp;=\u0026thinsp;22). There was a significant difference between the slopes of the two IQ groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that math group discrepancy increased with age for the lower verbal-IQ groups but reduced with age for the higher verbal-IQ ones.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGroup discrepancies in math proficiency between ASD and TD groups\u003c/h2\u003e \u003cp\u003eThe following results were from 66 samples of 33 studies, involving 2351 participants with ASD, and 1851 TD participants (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The ASD group performed worse in math than the TD control group (\u003cem\u003eHedges\u0026rsquo; g\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.676, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.141, 95%CI = [-.962, \u0026minus;\u0026thinsp;.390], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, k\u0026thinsp;=\u0026thinsp;66). This group discrepancy in math scores was reduced when all three types of IQs of the ASD group increased or as the full-scale and non-verbal IQs of the TD group decreased (Table\u0026nbsp;1). More analyses on the math-IQ relationship including correlations and differences are presented in Supplementary Materials. Additionally, this group discrepancy increased as the year of publication became more recent (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.031, 95%CI = [-.055, \u0026minus;\u0026thinsp;.006], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.019).\u003c/p\u003e \u003cp\u003eThe group discrepancy in math scores may be attributable to the differences in IQ scores, given that the ASD group had lower IQ scores across all three types than the TD group (\u003cem\u003eHedges\u0026rsquo; g\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.379/-.912/-.374; \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.111/.253/.146, 95%CI = [-.612, \u0026minus;\u0026thinsp;.145]/ [-1.441, \u0026minus;\u0026thinsp;.383]/ [-.680, \u0026minus;\u0026thinsp;.069], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003/.002/.020, k\u0026thinsp;=\u0026thinsp;39/41/40 for full-scale, verbal, and non-verbal IQs, respectively). To account for this difference, we statistically adjusted the ASD group\u0026rsquo;s IQ scores to match those of the TD group, but found that the ASD group still scored lower in math tests (intercept\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.237/-.215/-.232, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.101/.101/.092, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.034/.045/.021).\u003c/p\u003e \u003cp\u003eFurthermore, the group discrepancy was moderated by the interaction between full-scale IQ and age (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.159, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.044, 95%CI = [-.274, \u0026minus;\u0026thinsp;.045], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.016, k\u0026thinsp;=\u0026thinsp;48). Full-scale IQ scores ranged from 89.88 to 120.25, with the average at 102.48. For the ASD group with full-scale IQ below the mean, the slope of age was 0.134 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, simple intercept = -0.748, age\u0026thinsp;=\u0026thinsp;5.20\u0026ndash;26.40 years; k\u0026thinsp;=\u0026thinsp;22); for the ASD group with the IQ above the mean, the slope of age was \u0026minus;\u0026thinsp;0.186, (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, simple intercept = -0.178, age\u0026thinsp;=\u0026thinsp;5.98\u0026ndash;26.40; k\u0026thinsp;=\u0026thinsp;26). There was significant difference between the slopes of the two IQ groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that group discrepancy of math proficiency decreased with age for the lower full-scale IQ groups but increased with age for the higher IQ ones.\u003c/p\u003e \u003cp\u003eNo moderator effect on the group discrepancy in proficiency was found in symptom severity, age, or publication year (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGroup discrepancies in math variability between ASD and TD groups\u003c/h2\u003e \u003cp\u003eThe standard deviation of math scores of the ASD group (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 13.78, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 9.67; Range\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 0.05\u0026ndash;49.54, K\u0026thinsp;=\u0026thinsp;66) was 1.365\u0026thinsp;=\u0026thinsp;\u003cem\u003eexp\u003c/em\u003e(0.311) times greater than that of the TD group (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 11.31, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 8.45; Range\u003csub\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/sub\u003e = 0.01 to 49.66, k\u0026thinsp;=\u0026thinsp;66; \u003cem\u003elnVR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.311, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.052, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95% CI = [.207, .417]; Table\u0026nbsp;1). The group discrepancy was not moderated by any moderators or the interactions between IQ scores and age (Table\u0026nbsp;1). The ASD group still exhibited larger variability of math scores than the TD group when their full-scale, verbal and non-verbal IQ scores were statistically adjusted to match those of the TD group (intercept\u0026thinsp;=\u0026thinsp;.254/.210/.250; SE\u0026thinsp;=\u0026thinsp;.052/.046/.060, \u003cem\u003ep\u003c/em\u003es\u0026thinsp;\u0026lt;\u0026thinsp;.001, k\u0026thinsp;=\u0026thinsp;46/44/43).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur meta-analysis reveals that individuals with ASD overall exhibited significantly lower proficiency and greater variability in math ability compared to the general population as represented by norms of standardized math tests and the actual TD control groups included in each primary study. Therefore, individuals with ASD have unique educational needs in math, which should inform both the design of specialized educational interventions and the placement of students within appropriate educational settings. Moreover, this study identifies several factors moderating these group discrepancies, further elucidating the underlying reasons for these fundamental characteristics of the ASD population.\u003c/p\u003e \u003cp\u003eMath proficiency within the ASD groups, compared to the normative average, increases across all three IQ components (full-scale, verbal, and non-verbal IQ), reflecting a positive math-IQ relationship within the ASD population. This relationship is further supported by the moderate math-IQ correlations comparable to those in the general population (Peng et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, the math-IQ correlations and math-IQ deviation score do not differ between the ASD and TD groups, also suggesting that overall, the math abilities of individuals with ASD align with their intelligence (Supplementary Materials).\u003c/p\u003e \u003cp\u003eWhen compared with TD groups, the math proficiency of ASD groups also approaches that of TD groups as IQ levels increase. However, a group discrepancy persists even when both groups are statistically adjusted to the same IQ levels, suggesting that factors beyond intelligence influence math proficiency in individuals with ASD (see discussion below). Notably, our meta-analysis revealed that ASD groups, on average, had lower IQs than TD groups, indicating that many studies fail to rigorously control for this variable, representing a significant methodological issue in the current body of research.\u003c/p\u003e \u003cp\u003eAlthough math proficiency generally increases with intelligence, ASD individuals with different intelligence levels exhibit distinct developmental trajectories in math with age, as reflected in the interaction between full-scale IQ and age. Specifically, ASD individuals with lower full-scale IQs (89.88-100.89) show an increasing convergence in math proficiency with their TD peers over time. This trend suggests that ASD individuals with average intelligence levels can benefit from long-term math education similarly to TD peers. However, ASD individuals with higher full-scale IQs (103.70\u0026ndash;120.25) demonstrate a widening gap in math proficiency as they age relative to their TD peers. Although these ASD individuals also benefit from math education, they may encounter greater challenges in math learning compared to their TD peers with similar levels of intelligence. As the content of math one needs to learn becomes more complicated and challenging with age, obtaining advanced math knowledge requires additional abilities beyond intelligence, such as social communication and cooperation, academic motivation, self-efficacy, and mental pressure management (e.g., Ahmed et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rosenfeld et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). While TD individuals with above-average intelligence may have stronger proficiency in these non-cognitive aspects (Diseth et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mohzan et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), individuals with ASD, even those with average or above-average intelligence, may struggle in these areas and even experience more pressures and challenges in daily life as they age (Adreon et al., 2007; Anderson et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These disparities in non-cognitive skills may amplify the advantages TD individuals with higher intelligence have in mastering complex math concepts, whereas ASD individuals tend to fall further behind due to their relative disadvantages in these areas. This finding underscores the importance of providing life-span educational support tailored to the needs of ASD individuals with average or above-average intelligence, aiming to optimize their academic outcomes and prevent their math learning needs from being overlooked (Matson et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile our findings highlight the moderating effects of age and its interaction with intelligence, it should be noted that most data are derived from cross-sectional studies rather than longitudinal ones, which are scarce in this field (Kim et al., 2017; May et al., 2015; Wei et al., \u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). More longitudinal data are needed to control for interindividual differences and sociodemographic factors and clarify the developmental trajectory of math abilities in individuals with ASD.\u003c/p\u003e \u003cp\u003eNotably, the discrepancy in math proficiency between the ASD and TD groups has widened over the past few decades, signaling concerns about the effectiveness of math and cognitive interventions for individuals with ASD. While interventions have primarily focused on social interaction, language development, and behavioral issues, the enhancement of math skills has often been neglected. While the general population has benefited from more intensive and comprehensive math education, individuals with ASD may have been overlooked in this regard, placing them at a distinct disadvantage.\u003c/p\u003e \u003cp\u003eAdditionally, the widening group discrepancy over time may also reflect changes in research methodologies over the past decades (Adams et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Philip et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For example, recent studies may employ more rigorous recruitment strategies for TD samples, ensuring they are more closely matched to ASD samples on a range of factors. This, in turn, enhances the sensitivity of research tools in detecting group discrepancies.\u003c/p\u003e \u003cp\u003eWhen compared to standardized math test norms (SD\u0026thinsp;=\u0026thinsp;15), the variability in math performance within the ASD groups decreased as full-scale IQ increased. This suggests that individuals with higher IQs may experience fewer cognitive limitations, resulting in more stable math performance. Additionally, their math variability tended to increase with age. This may be due to that some individuals are able to manage the challenges of the growing complexity of math content as individuals mature, whereas others struggle more significantly as the demands of the subject matter increase. Furthermore, the increasing math variability may also reflect the diverse educational environments and varying intervention approaches encountered by individuals with ASD as they age (Kurth \u0026amp; Mastergeorge, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lewis \u0026amp; van Schalkwyk, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, the developmental trajectories of math variability within the ASD groups differ according to both age and intelligence. Specifically, ASD individuals with lower verbal IQs (63.00\u0026ndash;99.07; including individuals with average intelligence and intellectual disabilities) exhibit greater math variability as they aged, while those with higher verbal IQs (99.48\u0026ndash;117.31) show reduced variability. In terms of the math-IQ relationship, some individuals in the groups with lower verbal IQs demonstrate math abilities comparable to the general population, while others face considerable difficulties in math. These differences likely become more pronounced as these individuals age, with increasing math demands. Moreover, individuals in these groups may face broader life challenges, both in academic settings and beyond (e.g., Schneider \u0026amp; Niklas, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Tonizzi \u0026amp; Usai, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, ASD individuals with higher verbal IQs may experience fewer difficulties in math, as well as everyday life, and show greater alignment with their TD peers.\u003c/p\u003e \u003cp\u003eNevertheless, the math variability remains greater in individuals with ASD compared to TD controls, even when the IQs are matched between groups. Moreover, this ASD-TD group discrepancy was not moderated by any moderators included in this study, suggesting that the math variability of the individuals with ASD may be influenced by factors beyond intelligence, age, symptom severity and publication year. Non-cognitive factors, such as communication challenges, anxiety, and emotional issues (Maskey et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and comorbidities (e.g., ADHD, depression, epilepsy, Craig et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gadke et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Viscidi et al., 2013) may increase their math variability (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, due to the limited number of studies addressing these factors, they were not included in our analysis. More data on these factors should be accumulated to provide a comprehensive understanding of math abilities in the ASD population.\u003c/p\u003e \u003cp\u003eASD symptom severity did not moderate the group discrepancies in math proficiency or variability, consistent with previous findings (Hiniker et al., 2016; Iuculano et al., 2020; Miller et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zaidman-Zait et al., 2020). However, the use of composite ADOS scores may obscure relationships between math abilities and specific autism components (e.g., social affect, and restricted and repetitive behaviors). Additionally, the narrow range of ADOS scores across studies (group average ranged from 6.00 to 8.23) and the underrepresentation of participants with severe symptoms may limit the power to detect potential relationships. Collecting data from this subgroup is particularly challenging, highlighting the need to improve current assessments to better accommodate these individuals (Wang et al., \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Nevertheless, the relationship between math proficiency and ASD symptom severity remains insufficiently documented.\u003c/p\u003e \u003cp\u003eThis study provides two key insights that should be central to theoretical models addressing the math abilities of individuals with ASD: their lower proficiency and greater variability in math, as well as the positive math-IQ relationship.\u003c/p\u003e \u003cp\u003eWhile some theories highlight the strengths of math-related abilities in individuals with ASD (e.g., Baron-Cohen, 2007), our findings present a more nuanced perspective. We acknowledge that some individuals with ASD exhibit strong math abilities, but on the whole, their math proficiency is weaker than that of the general population, even when IQ levels are comparable. Moreover, the ASD-TD group discrepancy in math proficiency has widened over recent decades, raising concerns about the math development of the ASD population. Importantly, our analysis focuses on individuals with ASD who are able to engage with math tasks. However, a substantial proportion of individuals with ASD are unable to engage with such cognitive assessments, meaning that the general pattern of weaker math abilities is more representative of the broader ASD population.\u003c/p\u003e \u003cp\u003eOur study is also the first meta-analysis to demonstrate that individuals with ASD exhibit greater math variability than the general population. This parallels the substantial heterogeneity seen across many other domains within ASD such as social communication (Fountain et al., 2012), adaptive behaviors (Szatmari et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), language (Zheng et al., \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), cognitive processes (Charman et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and motor function (Fournier et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This finding may help reconcile previous inconsistent results regarding the strengths and weaknesses of math abilities in this population. Furthermore, the math variability remains greater in individuals with ASD, even when their intelligence or age are matched with TD groups, suggesting that additional factors, such as non-cognitive factors, comorbidities, or educational environments, may contribute to this variability.\u003c/p\u003e \u003cp\u003eFurthermore, the math-IQ relationship is critical within any theoretical framework for understanding the math abilities of individuals with ASD. While some individuals may demonstrate math-IQ deviations (e.g., Aagten-Murphy et al., 2015; Jones et al., 2009), our findings provide evidence for a moderate positive math-IQ relationship in the ASD population, comparable to that in the general population. This suggests that theoretical frameworks and interventions based on the math-IQ relationship in the general population may also apply to individuals with ASD.\u003c/p\u003e \u003cp\u003eImportantly, our findings expand the understanding of the math-IQ relationship by adopting a more refined, developmental perspective. We examined this relationship across subgroups with varying intelligence levels and considered the complex interactions between cognitive and non-cognitive factors, as well as educational influences. Individuals with ASD exhibit distinct developmental trajectories in math proficiency and variability depending on their intelligence level. Specifically, individuals with lower intelligence (most within the average range) tend to approximate the math proficiency of their TD peers over time while displaying increasing variability. In contrast, individuals with average or above-average intelligence exhibit a larger gap in math proficiency relative to similarly intelligent TD peers, along with reduced math variability as they age.\u003c/p\u003e \u003cp\u003eWe propose that intelligence plays a crucial role in supporting math learning in individuals with ASD, similar to its role in the general population. The support of cognitive abilities enables these individuals to benefit from long-term education, and higher intelligence may also help stabilize math performance by mitigating the negative effects of non-cognitive factors and environmental challenges. However, individuals with average or above-average intelligence may still experience limitations due to non-cognitive factors such as difficulties in social communication, emotional regulation, and behavioral challenges (Konstantareas \u0026amp; Stewart, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nyrenius \u0026amp; Billstedt, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Solish et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). While these individuals do gain from extended math education, the cumulative impact of these challenges may prevent them from achieving optimal outcomes comparable to their similarly intelligent TD peers over time.\u003c/p\u003e \u003cp\u003eTaken together, our findings emphasize the importance of adopting a developmental, dynamic perspective that considers the complex interplay between intelligence, non-cognitive abilities, and educational environments when examining math abilities in individuals with ASD. Future research and interventions should address these interactions to better support the math development of this population.\u003c/p\u003e"},{"header":"Method","content":" \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eLiterature searching and inclusion\u003c/h2\u003e \u003cp\u003eWe searched the following databases for studies investigating the math ability of the ASD population: PsycINFO, MEDLINE, ERIC and PsyARTICLES. In addition, grey literatures have been searched across multiple sources, including conference presentations in Association for Behavior Analysis International (ABAI), International Society for Autism Research (INSAR), Asia Pacific Autism Conference (APAC), Association for Behaviour Analysis Australia (ABA Australia), and Australasian Society for Autism Research (ASFAR), and dissertations in ProQuest. The following keywords were used in the full-text search: (autis*, Asperger) AND (math* OR arithmetic OR number OR numeracy OR algebra OR calcul*). Given that autism was included as a separate diagnostic category in the DSM III in 1980 (APA, 1980), articles published after 1980 were included. The literature search was completed on February 12th, 2024.\u003c/p\u003e \u003cp\u003eThe search yielded 44,353 articles, and 29,719 were kept after excluding duplicate ones (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To be included in the meta-analysis, a study must include participants diagnosed with ASD or autism or Asperger's syndrome; in addition, this study either (a) used standardized math tests and reported scores (including mean and standard deviation) for participants with ASD, regardless of including actual TD control groups, or (b) used non-standardized math tests and reported scores (including mean and standard deviation) for both participants with ASD and the actual TD control group. Studies qualified for (a) allowed to compare the math performance of participants with ASD in the standardized tests with the norm (M\u0026thinsp;=\u0026thinsp;100, SD\u0026thinsp;=\u0026thinsp;15). Studies qualified for (b) allowed to compare math performance between the ASD and the TD control groups. Studies qualified for meta-analyses were presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThree authors reviewed the titles and abstracts of articles, identifying 248 eligible studies. A total of 29471 articles were excluded because a very common word (\u0026ldquo;number\u0026rdquo;) was used as a keyword in search. Subsequently, researchers further read the full texts of the 248 articles and identified 78 articles relevant to the purpose of our study. A backward literature search, i.e., searching the references of the eligible articles, resulted in additional 12 articles. The initial pool of 90 articles, which broadly addresses the mathematical abilities of individuals with ASD as detailed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, was systematically evaluated against specific inclusion criteria for the meta-analysis. Among the 90 articles, three were doctoral dissertations (i.e., Alallawi, 2020; Gwaltney, 2012; Oliver, 2013), one was a master\u0026rsquo;s dissertation (Howard, 2020), and one doctoral dissertation was replaced by a published article using the same data set (Seh-Bae et al., 2015). Additionally, one was a poster presentation (Brown et al., 2019). This process resulted in a subset of 61 articles included in the quantitative meta-analyses, with their corresponding data presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTwo authors of this article reached 95.08% agreement in literature inclusion (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Three articles were discussed, and the authors agreed to exclude them from the meta-analysis due to no report of math scores (n\u0026thinsp;=\u0026thinsp;2), or only reporting non-standardized math scores without an actual TD control group (n\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVariable coding\u003c/h2\u003e \u003cp\u003eThe following information of studies was recorded (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e): year of the publication, sample size, age, math tests used in the study, math scores, intelligence tests used in the study, intelligence scores (including the full-scale, verbal and nonverbal IQ, if available), the math-IQ correlation coefficients (if available), ADOS scores, and comorbidity. If a study tested more than one ASD sample (e.g., different age groups), the information for each sample was recorded and coded independently. The above information for both the ASD and TD control groups were recorded, if available. Raw scores of the ADOS assessment were converted to the calibrated severity score (Gotham et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A particular study may provide only part of these variables. Inquiries for the missing information were emailed to corresponding authors of these studies, but no response was received.\u003c/p\u003e \u003cp\u003eSome studies provided more than one type of math scores and thus yielded more than one effect size. To account for the possible dependency among the estimated effect sizes from the same sample, robust variance estimation (RVE) was applied (Hedges et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rodgers \u0026amp; Pustejovsky, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData analyses\u003c/h2\u003e \u003cp\u003eAll meta-analyses were conducted using the R package \u003cem\u003erobumeta\u003c/em\u003e (Fisher \u0026amp; Tipton, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), in the following steps. First, we compared the math proficiency of the group with ASD to the norm or the actual TD control groups using random-effects meta-analyses (Fisher \u0026amp; Tipton, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Hedges\u0026rsquo; g was used as the effect size measure to quantify the standardized group mean difference. Cohen\u0026rsquo;s d was not used because it tends to overestimate the true effects in small samples (Cooper et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, p. 226), which is the case for many samples included in the present meta-analyses. When comparing the group with ASD to the norm, only samples with standardized math tests were included, and the mean and standard deviations of the norm (M\u0026thinsp;=\u0026thinsp;100; SD\u0026thinsp;=\u0026thinsp;15) were used to compute the effect sizes. When comparing ASD and TD control groups, the actual means and standard deviations of the two groups\u0026rsquo; performance were used to compute effect sizes. Notably, when effect sizes are derived from the same study or share data, dependent effect sizes would result in inaccurate publication bias tests, making it difficult to detect and correct publication bias in this situation (Rodgers \u0026amp; Pustejovsky, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, the results without correction were reported in the text.\u003c/p\u003e \u003cp\u003e Second, we used meta-regressions to explore whether the possible group discrepancies in math were moderated by intelligences (including full-scale, verbal and non-verbal IQs), symptom severity, age, publication year, and the interactions between intelligence and age. We further examined if the discrepancies in math performance between the ASD and the TD groups persisted when the IQs of the group with ASD were statistically adjusted to the same level of the TD control groups. To achieve this, we used the standardized mean difference (Hedges\u0026rsquo; g) in intelligence as a predictor for the standardized mean difference in math performance. The intercept from this meta-regression represents the expected standardized group difference in math performance when the group difference in intelligence is zero or when the ASD and control groups have equivalent IQ levels.\u003c/p\u003e \u003cp\u003eThird, we used the natural logarithm of variability ratio approach developed by Hedges and Nowell (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) to compare the variability of mathematical performance (i.e., standardized deviation) between the group with ASD and the norm (SD\u0026thinsp;=\u0026thinsp;15) or the TD control group (actual SDs). This approach uses the following statistic (i.e., lnVR) as the measure of effect size (i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{l}\\text{n}\\left(\\frac{{\\sigma\\:}_{\\text{T}}}{{\\sigma\\:}_{\\text{C}}}\\right)\\)\u003c/span\u003e\u003c/span\u003e): lnVR = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{ln}\\left(\\frac{{s}_{\\text{T}}}{{s}_{\\text{C}}}\\right)+\\frac{1}{2({n}_{\\text{T}}-1)}-\\frac{1}{2({n}_{\\text{C}}-1)}\\)\u003c/span\u003e\u003c/span\u003e where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{\\text{C}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{\\text{C}}\\)\u003c/span\u003e\u003c/span\u003e are the population standard deviations, the sample standard deviations, and the sample sizes of the ASD and control groups, respectively. The sampling variance of the lnVR statistic is computed by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{1}{2({n}_{\\text{T}}-1)}+\\frac{1}{2({n}_{\\text{C}}-1)}\\)\u003c/span\u003e\u003c/span\u003e. A value of zero for the effect size indicates no group discrepancy in variability; a positive value signifies that the variability of the ASD group is greater than that in the control group, while a negative value indicates the opposite. Then, we examined if the possible group discrepancy in variability was moderated by the factors mentioned above.\u003c/p\u003e \u003cp\u003eIn addition, to further explore the relationship between math ability and intelligence, we examined the correlation and deviation between them (see Supplemental Materials). We calculated the math-IQ Pearson correlation coefficients for ASD and TD control groups respectively. We also calculated the math-IQ deviation by subtracting the standardized math scores from the intelligence scores for the ASD and TD groups, respectively (e.g., Chen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Polo‑Blanco et al., 2024), and compared the difference between groups to examine if the math-IQ deviation differed between groups (Morris, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e*Aagten-Murphy, D., Attucci, C., Daniel, N., Klaric, E., Burr, D., \u0026amp; Pellicano, E. (2015). Numerical estimation in children with autism. \u003cem\u003eAutism Research,\u003c/em\u003e \u003cem\u003e8\u003c/em\u003e, 668-681.\u003c/li\u003e\n\u003cli\u003eAdams, D., Dargue, N., \u0026amp; Paynter, J. (2023). Longitudinal studies of challenging behaviours in autistic children and adults: A systematic review and meta-analysis. \u003cem\u003eClinical Psychology Review\u003c/em\u003e, 102320.\u003c/li\u003e\n\u003cli\u003eAdreon, D., \u0026amp; Durocher, J. S. (2007). Evaluating the college transition needs of individuals with high-functioning autism spectrum disorders. \u003cem\u003eIntervention in school and clinic\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(5), 271-279.\u003c/li\u003e\n\u003cli\u003eAhmed, W., Minnaert, A., van der Werf, G., \u0026amp; Kuyper, H. (2010). Perceived social support and early adolescents\u0026rsquo; achievement: The mediational roles of motivational beliefs and emotions. \u003cem\u003eJournal of youth and adolescence\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e, 36-46.\u003c/li\u003e\n\u003cli\u003e*Alallawi, B. (2020). \u003cem\u003eChildren with autism spectrum disorder in the context of Arab countries and cultures.\u003c/em\u003e [PhD thesis, University of Warwick]. University of Warwick Publications service \u0026amp; WRAP. http://webcat.warwick.ac.uk/record=b3467971~S15.\u003c/li\u003e\n\u003cli\u003eAmerican Psychiatric Association (APA). (1980). Diagnostic and\u003cem\u003e statistical manual of mental\u003c/em\u003e \u003cem\u003edisorders (3rd ed.)\u003c/em\u003e. Washington, DC: Author.\u003c/li\u003e\n\u003cli\u003eAmerican Psychiatric Association (APA). (2013). \u003cem\u003eDiagnostic\u003c/em\u003e \u003cem\u003eand statistical manual of mental\u003c/em\u003e \u003cem\u003edisorders (5th ed.)\u003c/em\u003e. Arlington, VA: Author.\u003c/li\u003e\n\u003cli\u003eAnderson, A. H., Stephenson, J., \u0026amp; Carter, M. (2017). A systematic literature review of the experiences and supports of students with autism spectrum disorder in post-secondary education. \u003cem\u003eResearch in Autism Spectrum Disorders\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e, 33-53.\u003c/li\u003e\n\u003cli\u003e*Assouline, S. G, Nicpon M. F., \u0026amp; Dockery, L. (2012). Predicting the academic achievement of gifted students with autism spectrum disorder. \u003cem\u003eJournal of Autism and\u003c/em\u003e \u003cem\u003eDevelopmental Disorders,\u003c/em\u003e \u003cem\u003e42\u003c/em\u003e(9), 1781-1789.\u003c/li\u003e\n\u003cli\u003e*Bangel, K. A., Batty, M., Ye, A. X., Meaux, E., Taylor, M. J., \u0026amp; Doesburg, S. M. (2014). Reduced beta band connectivity during number estimation in autism. \u003cem\u003eNeuroImage: Clinical,\u003c/em\u003e \u003cem\u003e6\u003c/em\u003e, 202-213.\u003c/li\u003e\n\u003cli\u003eBaron-Cohen, S., Wheelwright, S., Burtenshaw, A., \u0026amp; Hobson, E. (2007). Mathematical talent is linked to autism. \u003cem\u003eHuman\u003c/em\u003e \u003cem\u003eNature,\u003c/em\u003e \u003cem\u003e18\u003c/em\u003e, 125-131.\u003c/li\u003e\n\u003cli\u003eBartfai, A., Nyman, H., \u0026amp; Stegman, B. (1992). \u003cem\u003eWAIS-R manual.\u003c/em\u003e Stockholm: Psykologiforlaget AB.\u003c/li\u003e\n\u003cli\u003e*Brown, H. M., Trafford, L., Gaudet, E., Labont\u0026eacute;, C., Howard, K., Del Colle, R., et al. (2019, May). \u003cem\u003eChallenging Stereotypes of Math Giftedness and Math Disability in Students with Autism Spectrum Disorder (ASD)\u003c/em\u003e [Poster Presentation]. International Society for Autism Research 2019 Palais des congres de Montreal, Montreal, Qu\u0026eacute;bec Canada. https://insar.confex.com/insar/2019/webprogram/Paper32162.html\u003c/li\u003e\n\u003cli\u003eBrown, V. L., Cronin, M. E., \u0026amp; McEntire, E. (1994). \u003cem\u003eTest of mathematical abilities (2nd ed., TOMA-2). \u003c/em\u003eAustin, TX: Pro- Ed.\u003c/li\u003e\n\u003cli\u003e*Bullen, J. C., Lerro, L. S., Zajic, M., Mcintyre, N., \u0026amp; Mundy, P. (2020). A developmental study of mathematics in children with autism spectrum disorder, symptoms of attention deficit hyperactivity disorder, or typical development. \u003cem\u003eJournal of\u003c/em\u003e \u003cem\u003eAutism and Developmental Disorders,\u003c/em\u003e \u003cem\u003e50\u003c/em\u003e(6), 4463-4476.\u003c/li\u003e\n\u003cli\u003e*Bullen, J. C., Zajic, M. C., McIntyre, N., Solari, E., \u0026amp; Mundy, P. (2022). Patterns of math and reading achievement in children and adolescents with autism spectrum disorder. \u003cem\u003eResearch in Autism Spectrum Disorders\u003c/em\u003e, \u003cem\u003e92\u003c/em\u003e, 101933. https://doi.org/10.1016/j.rasd.2022.101933.\u003c/li\u003e\n\u003cli\u003eCattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. \u003cem\u003eJournal of Educational Psychology, 54\u003c/em\u003e(1), 1\u0026ndash;22. https://doi.org/10.1037/h0046743\u003c/li\u003e\n\u003cli\u003eCenter for Disease Control. (2020b, December 6). \u003cem\u003eScreening and diagnosis of\u003c/em\u003e \u003cem\u003eautism spectrum disorder for\u003c/em\u003e \u003cem\u003ehealth care providers.\u003c/em\u003e Center for Disease Control. https://www.cdc.gov/ncbddd/autism/hcpscreening.html\u003c/li\u003e\n\u003cli\u003eCharman, T., Jones, C. S., Pickles, A., Simonoff, E., Baird, G., \u0026amp; Happ\u0026eacute;, F. (2011). Defining the cognitive phenotype of autism. \u003cem\u003eBrain Research\u003c/em\u003e, \u003cem\u003e1380\u003c/em\u003e, 10-21. \u003c/li\u003e\n\u003cli\u003eChen, L., Abrams, D. M., Rosenberg-Lee, M., Iuculano, T., Wakeman, H. N., Prathap, S., Chen, T., \u0026amp; Menon, V. (2019). Quantitative Analysis of Heterogeneity in Academic Achievement of Children With Autism. \u003cem\u003eClinical Psychological Science\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 362\u0026ndash;380. \u003c/li\u003e\n\u003cli\u003eChiang, H. M., \u0026amp; Lin, Y. H. (2007). Mathematical ability of students with Asperger syndrome and high-functioning autism: A review of literature. \u003cem\u003eAutism,\u003c/em\u003e 11, 547-556.\u003c/li\u003e\n\u003cli\u003eClaessens, A., \u0026amp; Engel, M. (2013). How important is where you start? Early mathematics knowledge and later school success. \u003cem\u003eTeachers College Record,\u003c/em\u003e \u003cem\u003e115\u003c/em\u003e(6), 189-210.\u003c/li\u003e\n\u003cli\u003eConnolly, A. J. (2007). \u003cem\u003eKeyMath-3 Diagnostic Assessment Can. Ed. \u003c/em\u003eMinneapolis, MN: Pearson.\u003c/li\u003e\n\u003cli\u003eCooper, H., Hedges, L. V. \u0026amp; Valentine, J. C. (2009).\u003cem\u003e Handbook\u003c/em\u003e \u003cem\u003eof research synthesis and meta-analysis (2nd ed).\u003c/em\u003e New York: Russell Sage Foundation.\u003c/li\u003e\n\u003cli\u003eCowan, R., \u0026amp; Frith, C. (2009). Do calendrical savants use calculation to answer date questions? A functional magnetic resonance imaging study. \u003cem\u003ePhilosophical Transactions of the\u003c/em\u003e \u003cem\u003eRoyal Society of London B,\u003c/em\u003e \u003cem\u003e364,\u003c/em\u003e 1417-1424.\u003c/li\u003e\n\u003cli\u003eCraig, F., Lamanna, A.L., Margari, F., Matera, E., Simone, M., \u0026amp; Margari, L. (2015). Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder: Searching for Distinctive/Common Clinical Features. \u003cem\u003eAutism Research\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 328-337.\u003c/li\u003e\n\u003cli\u003ede Vos, T. (1992). \u003cem\u003eTempo Test Rekenen.\u003c/em\u003e Amsterdam: Pearson Education.\u003c/li\u003e\n\u003cli\u003eDekker, R., Dekker, P. H., \u0026amp; Mulder, J. L. (2004). \u003cem\u003eKaufman\u003c/em\u003e \u003cem\u003eAdolescent and Adult Intelligenc Test (KAIT).\u003c/em\u003e Leiden, The Netherland: PITS.\u003c/li\u003e\n\u003cli\u003eDiseth, \u0026Aring;., Meland, E., \u0026amp; Breidablik, H. J. (2014). Self-beliefs among students: Grade level and gender differences in self-esteem, self-efficacy and implicit theories of intelligence. \u003cem\u003eLearning and Individual Differences\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e, 1-8.\u003c/li\u003e\n\u003cli\u003eDuncan, G. J., Dowsett, C. J., \u0026amp; Japel, C. (2007). School readiness and later achievement. \u003cem\u003eDevelopmental Psychology,\u003c/em\u003e \u003cem\u003e43\u003c/em\u003e, 1428-1466.\u003c/li\u003e\n\u003cli\u003eEaves, L. C., \u0026amp; Ho, H. H. (1997). School placement and academic achievement in children with autistic spectrum disorders. \u003cem\u003eJournal of Developmental and Physical\u003c/em\u003e \u003cem\u003eDisabilities,\u003c/em\u003e \u003cem\u003e9\u003c/em\u003e(4), 277-291. \u003c/li\u003e\n\u003cli\u003eEhlers, S., Nyden, A., Gillberg, C., Sandberg, A. D., Hjelmqulst, E., \u0026amp; Oden, A. (1997). Asperger syndrome, autism and attention disorders: A comparative study of the cognitive profiles of 120 children. \u003cem\u003eJournal of Child Psychology and\u003c/em\u003e \u003cem\u003ePsychiatry,\u003c/em\u003e \u003cem\u003e38\u003c/em\u003e, 207-17.\u003c/li\u003e\n\u003cli\u003eElliot, C. D. (1990). \u003cem\u003eDifferential Ability Scales: Introductory and technical handbook\u003c/em\u003e. New York, NY: Psychological Corp.\u003c/li\u003e\n\u003cli\u003e*Estes, A., Rivera, V., Bryan, M., Cali, P., \u0026amp; Dawson, G. (2011). Discrepancies between academic achievement and intellectual ability in higher-functioning school-aged children with autism spectrum disorder. \u003cem\u003eJournal of Autism\u003c/em\u003e \u003cem\u003eand Developmental Disorders,\u003c/em\u003e \u003cem\u003e41\u003c/em\u003e(8), 1044-1052.\u003c/li\u003e\n\u003cli\u003eFarag, S. (2011). \u003cem\u003eStanford-Binet Intelligence Scales 5\u003csup\u003eth\u003c/sup\u003e (SBIS-IV)\u003c/em\u003e. Cairo: Anglo-Egyptian Bookshop.\u003c/li\u003e\n\u003cli\u003eFisher, Z., \u0026amp; Tipton, E. (2015). \u003cem\u003erobumeta: An R-package for robust variance estimation in meta-analysis\u003c/em\u003e. arXiv preprint arXiv:1503.02220.\u003c/li\u003e\n\u003cli\u003e*Foley-Nicpon, M., Assouline, S. G., \u0026amp; Stinson, R. D. (2012). Cognitive and academic distinctions between gifted student with autism and Asperger syndrome. \u003cem\u003eGifted Child\u003c/em\u003e \u003cem\u003eQuarterly, 52\u003c/em\u003e(2), 77-89.\u003c/li\u003e\n\u003cli\u003eFombonne, E. (2003). Epidemiological surveys of autism and other pervasive developmental disorders: An update. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(4), 365\u0026ndash;382.\u003c/li\u003e\n\u003cli\u003eFournier, K. A., Hass, C. J., Naik, S. K., Lodha, N., \u0026amp; Cauraugh, J. H. (2010). Motor coordination in autism spectrum disorders: A synthesis and meta-analysis. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e, 1227-1240 .\u003c/li\u003e\n\u003cli\u003e*Furlano, R., Kelley, E. A., Hall, L., \u0026amp; Wilson, D. E. (2015). Self-perception of competencies in adolescents with autism spectrum disorders. \u003cem\u003eAutism Research, 8\u003c/em\u003e(6), 761-770.\u003c/li\u003e\n\u003cli\u003eGadke, D. L., McKinney, C., \u0026amp; Oliveros, A. (2016). Autism spectrum disorder symptoms and comorbidity in emerging adults. \u003cem\u003eChild Psychiatry \u0026amp; Human Development\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e, 194-201. \u003c/li\u003e\n\u003cli\u003e*Gagnon, L., Mottron, L., Bherer, L., \u0026amp; Joanette, Y. (2004). Quantification judgement in high functioning autism: Superior or different? \u003cem\u003eJournal of Autism and Developmental\u003c/em\u003e \u003cem\u003eDisorders,\u003c/em\u003e \u003cem\u003e34\u003c/em\u003e(6), 679.\u003c/li\u003e\n\u003cli\u003eGeary, D. C. (2004). Mathematics and learning disabilities. \u003cem\u003eJournal of Learning Disabilities,\u003c/em\u003e \u003cem\u003e37\u003c/em\u003e, 4-15.\u003c/li\u003e\n\u003cli\u003eGeary, D. C. (2011). Cognitive predictors of achievement growth in mathematics: a 5-year longitudinal study. \u003cem\u003eDevelopmental Psychology,\u003c/em\u003e \u003cem\u003e47\u003c/em\u003e (6), 1539-1552.\u003c/li\u003e\n\u003cli\u003eGevarter, C., Bryant, D.P., Bryant, B. et al. (2016). Mathematics \u003c/li\u003e\n\u003cli\u003eInterventions for Individuals with Autism Spectrum Disorder: A Systematic Review. \u003cem\u003eReview Journal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 224\u0026ndash;238.\u003c/li\u003e\n\u003cli\u003eGinsburg, H., \u0026amp; Baroody, A. J. (2003). \u003cem\u003eTEMA-3: Test of early mathematics ability\u003c/em\u003e. Austin, TX: Pro-ed.\u003c/li\u003e\n\u003cli\u003e*Goldstein, G., Minshew, N. J., \u0026amp; Siegal, D. J. (1994). Age differences in academic achievement in high-functioning autistic individuals.\u003cem\u003e Journal of Clinical and Experimental\u003c/em\u003e \u003cem\u003eNeuropsychology,\u003c/em\u003e \u003cem\u003e16\u003c/em\u003e, 671-680.\u003c/li\u003e\n\u003cli\u003e*Goldstein, G., Beers, S. R., Siegel, D. J., \u0026amp; Minshew, N. J. (2001). A comparison of WAIS-R profiles in adults with high-functioning autism or differing subtypes of learning disability. \u003cem\u003eApplied Neuropsychology,\u003c/em\u003e \u003cem\u003e8\u003c/em\u003e(3), 148-154.\u003c/li\u003e\n\u003cli\u003eGotham, K., Pickles, A., \u0026amp; Lord, C. (2009). Standardizing ADOS scores for a measure of severity in autism spectrum disorders.\u003cem\u003e Journal of Autism and Developmental Disorders,\u003c/em\u003e \u003cem\u003e39\u003c/em\u003e(5), 693-705.\u003c/li\u003e\n\u003cli\u003e*Griswold, D. E., Barnhill, G. P., Myles, B. S., Hagiwara, T., \u0026amp; Simpson, R. L. (2002). Asperger syndrome and academic achievement. \u003cem\u003eFocus on Autism and Other Developmental\u003c/em\u003e \u003cem\u003eDisabilities,\u003c/em\u003e \u003cem\u003e17\u003c/em\u003e(2), 94-102.\u003c/li\u003e\n\u003cli\u003e*Gwaltney, M. V. (2012). Effects of IQ, memory and ADHD symptoms on achievement in students with high functioning autism. \u003cem\u003eUniversity of California, Davis, ProQuest\u003c/em\u003e \u003cem\u003eDissertations Publishing,\u003c/em\u003e 2012. 3555314.\u003c/li\u003e\n\u003cli\u003eHapp\u0026eacute;, F. (1999). Autism: cognitive deficit or cognitive style?. \u003cem\u003eTrends in cognitive sciences\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(6), 216-222.\u003c/li\u003e\n\u003cli\u003eHapp\u0026eacute;, F. (2011). Criteria, categories, and continua: Autism and related disorders in DSM-5. \u003cem\u003eJournal of the American\u003c/em\u003e \u003cem\u003eAcademy of Child \u0026amp; Adolescent Psychiatry,\u003c/em\u003e \u003cem\u003e50\u003c/em\u003e(6), 540-542.\u003c/li\u003e\n\u003cli\u003eHedges, L. V., \u0026amp; Friedman, L. (1993). Gender differences in variability in intellectual abilities: A reanalysis of Feingold\u0026rsquo;s results. \u003cem\u003eReview of Educational Research, 63\u003c/em\u003e(1), 94-105.\u003c/li\u003e\n\u003cli\u003eHedges, L.V., \u0026amp; Nowell, A. (1995). Sex differences in mental test scores, variability, and numbers of high-scoring individuals. \u003cem\u003eScience,\u003c/em\u003e \u003cem\u003e269\u003c/em\u003e, 41-45.\u003c/li\u003e\n\u003cli\u003eHedges, L. V., Tipton, E., \u0026amp; Johnson, M. C. (2010). Robust variance estimation in meta‐regression with dependent effect size estimates. \u003cem\u003eResearch synthesis methods\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 39-65.\u003c/li\u003e\n\u003cli\u003e*Hetzroni, O., Agada, H., \u0026amp; Leikin, M. (2019). Creativity in autism: An examination of general and mathematical creative thinking among children with autism spectrum disorder and children with typical development. \u003cem\u003eJournal of Autism and\u003c/em\u003e \u003cem\u003eDevelopmental Disorders,\u003c/em\u003e \u003cem\u003e49\u003c/em\u003e(9), 1-12.\u003c/li\u003e\n\u003cli\u003e*Higashionna, T., Iwanaga, R., Tokunaga, A., Nakai, A., Tanaka, K., \u0026amp; Tanaka, G. (2017). The relationship between motor coordination ability, cognitive ability, and academic achievement in Japanese children with autism spectrum disorder and attention deficit/hyperactivity disorder. \u003cem\u003eBrain Science, 12\u003c/em\u003e, 674-684.\u003c/li\u003e\n\u003cli\u003e*Hiniker, A., Rosenberg-Lee, M., \u0026amp; Menon, V. (2016). Distinctive role of symbolic number sense in mediating the mathematical abilities of children with autism. \u003cem\u003eJournal of\u003c/em\u003e \u003cem\u003eAutism and Developmental Disorders,\u003c/em\u003e \u003cem\u003e46\u003c/em\u003e, 1268-1281.\u003c/li\u003e\n\u003cli\u003e*Howard, K. (2020). \u003cem\u003eThe Role of Achievement Emotions in Math Performance Outcomes of Students with Autism Spectrum Disorder\u003c/em\u003e. [Master dissertation, University of Alberta]. University of Alberta Library. https://doi.org/10.7939/r3-aee0-c674.\u003c/li\u003e\n\u003cli\u003eHumphrey, N. (2010). Including pupils with autistic spectrum disorders in mainstream schools. \u003cem\u003eSupport for Learning,\u003c/em\u003e \u003cem\u003e23\u003c/em\u003e(1), 41-47.\u003c/li\u003e\n\u003cli\u003eIuculano, T., Rosenberg-Lee, M., Supekar, K., Lynch, C. J., Khouzam, A., \u0026amp; Phillips, J., Uddin, L. Q., \u0026amp; Menon, V. (2014). Brain organization underlying superior mathematical abilities in children with autism. \u003cem\u003eBiological Psychiatry,\u003c/em\u003e \u003cem\u003e75\u003c/em\u003e(3), 223-230.\u003c/li\u003e\n\u003cli\u003e*Iuculano, T., Padmanabhan A., Chen, L., Nicholas, J., Mitsven, S., \u0026amp; de Los Angeles, C. (2020). Neural correlates of cognitive variability in childhood autism and relation to heterogeneity in decision-making dynamics. \u003cem\u003eDevelopmental\u003c/em\u003e \u003cem\u003eCognitive Neuroscience,\u003c/em\u003e \u003cem\u003e42\u003c/em\u003e, 1-12.\u003c/li\u003e\n\u003cli\u003eJarrold, C., \u0026amp; Russell, J. (1997). Counting abilities in autism: Possible implications for central coherence theory. \u003cem\u003eJournal of\u003c/em\u003e \u003cem\u003eAutism and Developmental Disorders,\u003c/em\u003e \u003cem\u003e27\u003c/em\u003e(1), 25-37.\u003c/li\u003e\n\u003cli\u003eJastak, J. F., \u0026amp; Jastak, S. R. (1965). \u003cem\u003eThe Wide Range\u003c/em\u003e \u003cem\u003eAchievement Test (WRAT) Manual.\u003c/em\u003e Wilmington, DE: Guidance Associates.\u003c/li\u003e\n\u003cli\u003eJastak, J. E., \u0026amp; Jastak, S. R. (1984). \u003cem\u003eWide Range Achievement\u003c/em\u003e \u003cem\u003eTest Revised (WART-R). \u003c/em\u003eWilmington, DE: Jastak Assoc.\u003c/li\u003e\n\u003cli\u003e*John, T. S., Dawson, G., \u0026amp; Estes, A. (2017). Brief report: Executive function as a predictor of academic achievement in school-aged children with ASD. \u003cem\u003eJournal of Autism and\u003c/em\u003e \u003cem\u003eDevelopmental Disorders,\u003c/em\u003e \u003cem\u003e48\u003c/em\u003e, 276-283.\u003c/li\u003e\n\u003cli\u003e*Jones, C. R., Happ\u0026eacute;, F., Golden, H., Marsden, A. J., Tregay, J., Simonoff, E., \u0026amp; Charman, T. (2009). Reading and arithmetic in adolescents with autism spectrum disorders: Peaks and dips in attainment. \u003cem\u003eNeuropsychology, 23\u003c/em\u003e(6), 718-728. \u003c/li\u003e\n\u003cli\u003eKaufman, A. S., \u0026amp; Kaufman, N. L. (1985). \u003cem\u003eKaufman Test of\u003c/em\u003e \u003cem\u003eEducational Achievement (K-TEA)\u003c/em\u003e. Circle Pines, MN: American Guidance Service.\u003c/li\u003e\n\u003cli\u003eKaufman, A. S., \u0026amp; Kaufman, N. L. (2004). \u003cem\u003eKaufman Brief\u003c/em\u003e \u003cem\u003eIntelligence Test, Second Edition (KBIT-2).\u003c/em\u003e Bloomington, MN: NCS Pearson Inc.\u003c/li\u003e\n\u003cli\u003eKeen, D., Webster, A., \u0026amp; Ridley, G. (2016). How well are children with autism spectrum disorder doing academically at school? An overview of the literature. \u003cem\u003eAutism,\u003c/em\u003e \u003cem\u003e20\u003c/em\u003e(3), 276-294.\u003c/li\u003e\n\u003cli\u003e*Kim, S. H., Bal, V. H., \u0026amp; Lord, C. (2017). Longitudinal follow-up of academic achievement in children with autism from age 2 to 18. \u003cem\u003eJournal of Child Psychology and Psychiatry,\u003c/em\u003e \u003cem\u003e59\u003c/em\u003e(3), 258-267.\u003c/li\u003e\n\u003cli\u003e*Kim, S. H., Buzzell, G., Faja, S., Choi, Y. B., \u0026amp; Fox, N. (2020). Neural dynamics of executive function in cognitively able kindergarteners with autism spectrum disorders as predictors of concurrent academic achievement. \u003cem\u003eAutism,\u003c/em\u003e \u003cem\u003e24\u003c/em\u003e(3), 1-15.\u003c/li\u003e\n\u003cli\u003eKonstantareas, M. M., \u0026amp; Stewart, K. (2006). Affect regulation and temperament in children with autism spectrum disorder. \u003cem\u003eJournal of Autism and Developmental Disorders, 36\u003c/em\u003e, 143-154.\u003c/li\u003e\n\u003cli\u003eKurth, J., \u0026amp; Mastergeorge, A. M. (2010). Individual education plan goals and services for adolescents with autism: Impact of age and educational setting. \u003cem\u003eThe Journal of Special Education\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(3), 146-160. \u003c/li\u003e\n\u003cli\u003eLewis, A. S., \u0026amp; van Schalkwyk, G. I. (2020). Systematic review: distribution of age and intervention modalities in therapeutic clinical trials for autism spectrum disorder. \u003cem\u003eJournal of autism and developmental disorders\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(6), 2208-2216.\u003c/li\u003e\n\u003cli\u003eLindor, E., Sivaratnam, C., May, T., Stefanac, N., Howells, K., \u0026amp; Rinehart, N. (2019). Problem behavior in autism spectrum disorder: considering core symptom severity and accompanying sleep disturbance. \u003cem\u003eFrontiers in Psychiatry\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e, 487-497. \u003c/li\u003e\n\u003cli\u003eMaskey, M., Warnell, F., Parr, J.R., Couteur, A. L., \u0026amp; McConachie, H. (2013). Emotional and Behavioural Problems in Children with Autism Spectrum Disorder. \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e\u003cem\u003e3\u003c/em\u003e, 851-859.\u003c/li\u003e\n\u003cli\u003eMather, N., \u0026amp; Woodcock, R. W. (2001). \u003cem\u003eExaminer\u0026apos;s Manual. In\u003c/em\u003e \u003cem\u003eWoodcock-Johnson III Tests of Achievement (WJ-III-ACH)\u003c/em\u003e. Itasca, IL: Riverside Publishing.\u003c/li\u003e\n\u003cli\u003eMatson, J. L., Cervantes, P. E., \u0026amp; Peters, W. J. (2016). Autism spectrum disorders: management over the lifespan. \u003cem\u003eExpert review of Neurotherapeutics\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(11), 1301-1310.\u003c/li\u003e\n\u003cli\u003e*May, T., Rinehart, N., Wilding, J., \u0026amp; Cornish, K., (2013). The role of attention in the academic attainment of children with autism spectrum disorder. \u003cem\u003eJournal of Autism and\u003c/em\u003e \u003cem\u003eDevelopmental Disorders,\u003c/em\u003e \u003cem\u003e43\u003c/em\u003e, 2147-2158.\u003c/li\u003e\n\u003cli\u003e*May, T., Rinehart, N. J., Wilding, J., \u0026amp; Cornish, K. (2015). Attention and basic literacy and numeracy in children with autism spectrum disorder: A one-year follow-up study. \u003cem\u003eResearch in Autism Spectrum Disorders,\u003c/em\u003e \u003cem\u003e9,\u003c/em\u003e 193-201.\u003c/li\u003e\n\u003cli\u003e*Mayes, S. D., \u0026amp; Calhoun, S. L. (2003a). Ability profiles in children with autism: Influence of age and IQ. \u003cem\u003eAutism,\u003c/em\u003e \u003cem\u003e7\u003c/em\u003e, 65-80.\u003c/li\u003e\n\u003cli\u003e*Mayes, S. D., \u0026amp; Calhoun, S. (2003b). Analysis of WISC\u0026ndash;III, Stanford\u0026ndash;Binet\u0026ndash;IV, and Academic Achievement Test Scores in children with autism. \u003cem\u003eJournal of Autism and\u003c/em\u003e \u003cem\u003eDevelopmental Disorders,\u003c/em\u003e \u003cem\u003e33\u003c/em\u003e, 65-80.\u003c/li\u003e\n\u003cli\u003e*Mayes, S. D., \u0026amp; Calhoun, S. (2006). Frequency of reading, math, and writing disabilities in children with clinical disorders. \u003cem\u003eLearning and Individual Differences,\u003c/em\u003e \u003cem\u003e16\u003c/em\u003e, 145-157.\u003c/li\u003e\n\u003cli\u003eMayes, S. D., \u0026amp; Calhoun, S. L. (2007). Learning, attention, writing, and processing speed in typical children and children with ADHD, autism, anxiety, depression, and oppositional-defiant disorder. \u003cem\u003eChild Neuropsychology,\u003c/em\u003e \u003cem\u003e13\u003c/em\u003e, 469-493.\u003c/li\u003e\n\u003cli\u003e*Mayes, S. D., \u0026amp; Calhoun, S. L. (2008). WISC-IV and WIAT-II profiles in children with high functioning autism. \u003cem\u003eJournal of\u003c/em\u003e \u003cem\u003eAutism and Developmental Disorders,\u003c/em\u003e \u003cem\u003e38\u003c/em\u003e, 428-439.\u003c/li\u003e\n\u003cli\u003e*Mayes, S. D., Waschbusch, D. A., Calhoun, S. L., \u0026amp; Mattison, R. E. (2019). How common are academic overachievement and underachievement in children with autism or ADHD? \u003cem\u003eJournal of Developmental and Physical Disabilities,\u003c/em\u003e \u003cem\u003e32\u003c/em\u003e(4), 775-783.\u003c/li\u003e\n\u003cli\u003eMayes, S. D., Waschbusch, D. A., Calhoun, S. L., \u0026amp; Mattison, R. E. (2020). Correlates of academic overachievement, nondiscrepant achievement, and learning disability in ADHD, autism, and general population samples. \u003cem\u003eExceptionality,\u003c/em\u003e \u003cem\u003e28\u003c/em\u003e(1), 1-16.\u003c/li\u003e\n\u003cli\u003eMazzocco, M. M. M., \u0026amp; Thompson, R. E. (2010). Kindergarten predictors of math learning disability. \u003cem\u003eLearning Disabilities\u003c/em\u003e \u003cem\u003eResearch, 20\u003c/em\u003e(3), 142-155. \u003c/li\u003e\n\u003cli\u003e*McCauley, J. B., Zajic, M. C., Oswald, T. M., Swain-Lerro, L. E., McIntyre, N. C., \u0026amp; Harris, M. A., et al. (2017). Brief report: Investigating relations between self-concept and performance in reading and math for school-aged children and adolescents with autism spectrum disorder. \u003cem\u003eJournal of\u003c/em\u003e \u003cem\u003eAutism and Developmental Disorders\u003c/em\u003e,\u003cem\u003e 48\u003c/em\u003e, 1825-1832.\u003c/li\u003e\n\u003cli\u003e*McDougal, E., Riby, D. M., \u0026amp; Hanley, M. (2020). Profiles of academic achievement and attention in children with and without autism spectrum disorder. \u003cem\u003eResearch in\u003c/em\u003e \u003cem\u003eDevelopmental Disabilities,\u003c/em\u003e \u003cem\u003e106\u003c/em\u003e, 103749.\u003c/li\u003e\n\u003cli\u003e*Mcknight, M. E., \u0026amp; Culotta, V. P. (2012). Comparing neuropsychological profiles between girls with Asperger\u0026rsquo;s disorder and girls with learning disabilities. \u003cem\u003eFocus on Autism\u003c/em\u003e \u003cem\u003eand Other Developmental Disabilities,\u003c/em\u003e \u003cem\u003e27\u003c/em\u003e(4), 247-253.\u003c/li\u003e\n\u003cli\u003eMeaux, E., Taylor, M. J., Pang, E. W., Vara, A. S., \u0026amp; Batty, M. (2014). Neural Substrates of Numerosity Estimation in Autism\u003cem\u003e.\u003c/em\u003e \u003cem\u003eHuman Brain Mapping\u003c/em\u003e\u003cem\u003e, \u003c/em\u003e\u003cem\u003e35\u003c/em\u003e, 4362-4385.\u003c/li\u003e\n\u003cli\u003eMiller, L. E., Burke, J. D., Troyb, E., Knoch, K, Herlihy, L. E., \u0026amp; Fein, D. A. (2016). Preschool predictors of school-age academic achievement in autism spectrum disorder. \u003cem\u003eClinical\u003c/em\u003e \u003cem\u003eNeuropsychologist,\u003c/em\u003e 1744-1765. \u003c/li\u003e\n\u003cli\u003e*Minshew, N. J., Muenz, L. R., Goldstein, G., \u0026amp; Payton, J. B. (1992). Neuropsychological functioning in nonmentally retarded autistic individuals. \u003cem\u003eJournal of Clinical and\u003c/em\u003e \u003cem\u003eExperimental Neuropsychology,\u003c/em\u003e \u003cem\u003e14\u003c/em\u003e(5), 749-761.\u003c/li\u003e\n\u003cli\u003e*Minshew, N. J., Goldstein, G., Taylor, H. G., \u0026amp; Siegel, D. J. (1994). Academic achievement in high functioning autistic individuals. \u003cem\u003eJournal of Clinical and Experimental Neuropsychology,\u003c/em\u003e \u003cem\u003e16\u003c/em\u003e(2), 261-270.\u003c/li\u003e\n\u003cli\u003eMorris, S. B. (2008). Estimating Effect Sizes From Pretest-Posttest-Control Group Designs. \u003cem\u003eOrganizational Research Methods\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 364\u0026ndash;386.\u003c/li\u003e\n\u003cli\u003eMohzan, M. A. M., Hassan, N., \u0026amp; Abd Halil, N. (2013). The influence of emotional intelligence on academic achievement. \u003cem\u003eProcedia-Social and Behavioral Sciences\u003c/em\u003e, \u003cem\u003e90\u003c/em\u003e, 303-312.\u003c/li\u003e\n\u003cli\u003e*Myles, B. S., Simpson, R. L., \u0026amp; Becker, J. (1994). An analysis of characteristics of students diagnosed with higher-functioning autism disorder. \u003cem\u003eExceptionality,\u003c/em\u003e \u003cem\u003e5\u003c/em\u003e(1), 19-30.\u003c/li\u003e\n\u003cli\u003eNyrenius, J., \u0026amp; Billstedt, E. (2020). The functional impact of cognition in adults with autism spectrum disorders. \u003cem\u003eNordic Journal of Psychiatry, 74\u003c/em\u003e(3), 220-225.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Connor, N., Cowan, R., \u0026amp; Samella, K. (2000). Calendrical calculation and intelligence. \u003cem\u003eIntelligence, 28\u003c/em\u003e(1), 31-48.\u003c/li\u003e\n\u003cli\u003e*Oswald, T. M., Beck, J. S., Iosif, A. M., McCauley, J. B., Gilhooly, L. J., Matter, J., \u0026amp; Solomon, M. (2016). Clinical and cognitive characteristics associated with mathematics problem solving in adolescents with autism spectrum disorder. \u003cem\u003eAutism Research, 9\u003c/em\u003e, 480-490.\u003c/li\u003e\n\u003cli\u003ePellicano, E. (2012). The development of executive function in autism. \u003cem\u003eAutism research and treatment\u003c/em\u003e, \u003cem\u003e2012\u003c/em\u003e(1), 146132.\u003c/li\u003e\n\u003cli\u003ePeng, P., Fei, W. T., Cui, W. C., \u0026amp; Lin, X. (2019). A meta-analysis on the relation between fluid intelligence and reading/mathematics: Effects of tasks, age, and social economics status. \u003cem\u003ePsychological Bulletin,\u003c/em\u003e \u003cem\u003e45\u003c/em\u003e(2), 189-236.\u003c/li\u003e\n\u003cli\u003ePennington, B. F., \u0026amp; Ozonoff, S. (1996). Executive functions and developmental psychopathology. \u003cem\u003eJournal of Child\u003c/em\u003e \u003cem\u003ePsychology and Psychiatry,\u003c/em\u003e \u003cem\u003e37\u003c/em\u003e(1), 51-87.\u003c/li\u003e\n\u003cli\u003ePhilip, R. C., Dauvermann, M. R., Whalley, H. C., Baynham, K., Lawrie, S. M., \u0026amp; Stanfield, A. C. (2012). A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. \u003cem\u003eNeuroscience \u0026amp; Biobehavioral Reviews\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(2), 901-942.\u003c/li\u003e\n\u003cli\u003ePostlethwaite, B. E. (2011). Fluid ability, crystallized ability, and performance across multiple domains. [Doctoral thesis, University of Iowa]. University of Iowa. https://doi.org/10.17077/etd.zopi8wvs \u003c/li\u003e\n\u003cli\u003eRaven, J., Raven, J. C., \u0026amp; Court, J. H. (1998). \u003cem\u003eManual for\u003c/em\u003e \u003cem\u003eRaven\u0026rsquo;s Progressive Matrices and Vocabulary Scales.\u003c/em\u003e San Antonio: Harcourt Assessment.\u003c/li\u003e\n\u003cli\u003eRaven, J., Raven, J. C., \u0026amp; Court, J. H. (2008). \u003cem\u003eRaven\u0026rsquo;s\u003c/em\u003e \u003cem\u003eEducational: Coloured Progressive\u003c/em\u003e \u003cem\u003eMatrices (CPM) and\u003c/em\u003e \u003cem\u003eCrichton Vocabulary Scale Manual.\u003c/em\u003e London: NCS Pearson.\u003c/li\u003e\n\u003cli\u003eRoberts, J., \u0026amp; Simpson, K. (2016). A review of research into stakeholder perspectives on inclusion of students with autism in mainstream schools. \u003cem\u003eInternational Journal of Inclusive Education\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(10), 1084-1096.\u003c/li\u003e\n\u003cli\u003eRodgers, M. A., \u0026amp; Pustejovsky, J. E. (2020). Evaluating meta-analytic methods to detect selective reporting in the presence of dependent effect sizes. \u003cem\u003ePsychological methods\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 141-160.\u003c/li\u003e\n\u003cli\u003eRoot, J. R., Ingelin, B., \u0026amp; Cox, S. K. (2021). Teaching mathematical word problem solving to students with autism spectrum disorder: A best-evidence synthesis. \u003cem\u003eEducation and Training in Autism and Developmental Disabilities\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e (4), 420-436.\u003c/li\u003e\n\u003cli\u003eRosenfeld, L. B., Richman, J. M., \u0026amp; Bowen, G. L. (2000). Social support networks and school outcomes: The centrality of the teacher. \u003cem\u003eChild and adolescent social work journal\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 205-226.\u003c/li\u003e\n\u003cli\u003e*Rumsey, J. M., \u0026amp; Hamburger, S. D. (1988). Neuropsychological findings in high-functioning men with infantile autism, residual state. \u003cem\u003eJournal of Clinical and Experimental Neuropsychology, 10\u003c/em\u003e(2), 201-221.\u003c/li\u003e\n\u003cli\u003e*Rumsey, J. M., \u0026amp; Hamburger, S. D. (1988). Neuropsychological divergence of high-level autism and severe dyslexia. \u003cem\u003eJournal of Autism and Developmental Disorders, 20\u003c/em\u003e, 155-168.\u003c/li\u003e\n\u003cli\u003eRust, J., Golombok, S., \u0026amp; Trickey, G. (1993). \u003cem\u003eWechsler\u003c/em\u003e \u003cem\u003eObjective Numerical Dimensions (WOND) Manual. \u003c/em\u003eLondon: The Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eSacks, O. (1995). The man who mistook his wife for a hat. \u003cem\u003eBritish Journal of Psychiatry, 166\u003c/em\u003e, 130-131.\u003c/li\u003e\n\u003cli\u003eSchneider, W., \u0026amp; Niklas, F. (2017). Intelligence and verbal short-term memory/working memory: Their interrelationships from childhood to young adulthood and their impact on academic achievement. \u003cem\u003eJournal of Intelligence\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(2), 26.\u003c/li\u003e\n\u003cli\u003e*Seh-Bae, Y., Chiang, H. M., \u0026amp; Hickson, L. (2015). Mathematical word problem solving ability of children with autism spectrum disorder and their typically developing peers. \u003cem\u003eJournal of Autism and Developmental Disorders,\u003c/em\u003e \u003cem\u003e45\u003c/em\u003e(7), 2200-2208.\u003c/li\u003e\n\u003cli\u003eSolish, A., Perry, A., \u0026amp; Minnes, P. (2010). Participation of children with and without disabilities in social, recreational and leisure activities. \u003cem\u003eJournal of Applied Research in\u003c/em\u003e \u003cem\u003eIntellectual Disabilities,\u003c/em\u003e \u003cem\u003e23\u003c/em\u003e, 226-236.\u003c/li\u003e\n\u003cli\u003eSouli\u0026egrave;res, I., Hubert, B., Rouleauc, N., Gagnone, L., Tremblay, P., Seron, X., \u0026amp; Mottron, L. (2010). Superior estimation abilities in two autistic spectrum children. \u003cem\u003eCognitive Neuropsychiatry,\u003c/em\u003e \u003cem\u003e27\u003c/em\u003e, 261-276.\u003c/li\u003e\n\u003cli\u003e*Stroizer, S., Hinton, V., Flores, M., \u0026amp; Terry, L. (2015). An investigation of the effects of CRA instruction and students with autism spectrum disorder. \u003cem\u003eEducation and Training in\u003c/em\u003e \u003cem\u003eAutism and Developmental Disabilities,\u003c/em\u003e \u003cem\u003e50\u003c/em\u003e(2), 223-236.\u003c/li\u003e\n\u003cli\u003eSzatmari, P., Georgiades, S., Duku, E., Bennett, T. A., Bryson, S., Fombonne, E., Mirenda, P., Roberts, W., Smith, I., M., Vaillancourt, T., Volden, J., Waddell, C., Zwaigenbaum, L., Elsabbagh, M., \u0026amp; Thompson, A. (2015). Developmental trajectories of symptom severity and adaptive functioning in an inception cohort of preschool children with autism spectrum disorder. \u003cem\u003eJAMA Psychiatry\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e(3), 276\u0026ndash;283.\u003c/li\u003e\n\u003cli\u003eThe Psychological Corp. (1992). \u003cem\u003eWechsler Individual\u003c/em\u003e \u003cem\u003eAchievement Test (WIAT).\u003c/em\u003e New York.\u003c/li\u003e\n\u003cli\u003eTiteca, D., Roeyers, H., Josephy, H., Ceulemans, A., \u0026amp; Desoete, A. (2014). Preschool predictors of mathematics in first grade children with autism spectrum disorder. \u003cem\u003eResearch in\u003c/em\u003e \u003cem\u003eDevelopmental Disabilities,\u003c/em\u003e \u003cem\u003e35\u003c/em\u003e(11), 2714-2727.\u003c/li\u003e\n\u003cli\u003eTiteca, D., Roeyers, H., \u0026amp; Desoete, A. (2015a). Early numerical competencies in 4- and 5- year- old children with autism spectrum disorder. \u003cem\u003eFocus on Autism and Other\u003c/em\u003e \u003cem\u003eDevelopmental Disabilities,\u003c/em\u003e \u003cem\u003e32\u003c/em\u003e(4), 279-292.\u003c/li\u003e\n\u003cli\u003e*Titeca, D., Roeyers, H., \u0026amp; Desoete, A. (2015b). Early numerical competencies in 5- and 6-year-old children with autism spectrum disorder. \u003cem\u003eEarly Education and Development, 26\u003c/em\u003e(7), 1012-1034.\u003c/li\u003e\n\u003cli\u003eTonizzi, I., \u0026amp; Usai, M. C. (2023). Math abilities in autism spectrum disorder: A meta-analysis. \u003cem\u003eResearch in Developmental Disabilities\u003c/em\u003e, \u003cem\u003e139\u003c/em\u003e, 104559.\u003c/li\u003e\n\u003cli\u003eTonizzi, I., \u0026amp; Usai, M. C. (2024). Cognitive correlates of math abilities in autism spectrum disorder. \u003cem\u003ePLoS ONE\u003c/em\u003e, 19(9), e0310525.\u003c/li\u003e\n\u003cli\u003e*Tops, W., Bergh, A. V. D., Noens, I., \u0026amp; Baeyens, D. (2017). A multi-method assessment of study strategies in higher education students with an autism spectrum disorder. \u003cem\u003eLearning and\u003c/em\u003e \u003cem\u003eIndividual Differences,\u003c/em\u003e \u003cem\u003e59\u003c/em\u003e, 141-148.\u003c/li\u003e\n\u003cli\u003e*Troyb, E., Orinstein, A., Tyson, K., Helt, M., Eigsti, I. M., Stevens, M., \u0026amp; Fein, D. (2014). Academic abilities in children and adolescents with a history of autism spectrum disorders who have achieved optimal outcomes. \u003cem\u003eAutism,\u003c/em\u003e \u003cem\u003e18\u003c/em\u003e(3), 233-243.\u003c/li\u003e\n\u003cli\u003eTuri, M., Burr, D. C., Igliozzi, R., Aagten-Murphy, D., Muratori, F., \u0026amp; Pellicano, E., et al. (2015). Children with autism spectrum disorder show reduced adaptation to number. \u003cem\u003eProceedings of the\u003c/em\u003e \u003cem\u003eNational Academy of Sciences of the\u003c/em\u003e \u003cem\u003eUnited States of America,\u003c/em\u003e \u003cem\u003e112\u003c/em\u003e(25), 7868-7872.\u003c/li\u003e\n\u003cli\u003eWallace, G. L., Dudley, K., Anthony, L., Pugliese, C. E., Orionzi, B., Clasen, L., \u0026amp; Clasen, L., et al. (2017). Divergence of age-related differences in social-communication: Improvements for typically developing youth but declines for youth with autism spectrum disorder. \u003cem\u003eJournal of autism and developmental disorders\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e, 472-479.\u003c/li\u003e\n\u003cli\u003eWang, Q., Han, Z., Hu, X., Feng, S., Wang, H., Liu, T., \u0026amp; Yi, L. (2020). Autism symptoms modulate interpersonal neural synchronization in children with autism spectrum disorder in cooperative interactions. \u003cem\u003eBrain Topography\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 112-122.\u003c/li\u003e\n\u003cli\u003e*Wang, L., Liang, X., Jiang, B., Wu, Q., \u0026amp; Jiang, L. (2022). What ability can predict mathematics performance in typically developing preschoolers and those with autism spectrum disorder? \u003cem\u003eJournal of Autism and Developmental Disorders\u003c/em\u003e. https://doi.org/10.1007/s10803-022-05454-w \u003c/li\u003e\n\u003cli\u003eWang, X. S., Perry, L. B., Malpique, A., \u0026amp; Ide, T. (2023). Factors predicting mathematics achievement in PISA: a systematic review. \u003cem\u003eLarge-Scale Assessments in Education\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 24.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1949). \u003cem\u003eWechsler Intelligence Scale for Children (WISC).\u003c/em\u003e New York: The Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1955). \u003cem\u003eWechsler Adult Intelligence Scale (WAIS) Manual.\u003c/em\u003e New York: Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1974). \u003cem\u003eWechsler Intelligence Scale for Children-Revised (WISC-R).\u003c/em\u003e New York: The Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1981). \u003cem\u003eWechsler Adult Intelligence Scale-Revised (WAIS-R). \u003c/em\u003eNew York: The Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1991). \u003cem\u003eWechsler Intelligence Scale for Children\u003c/em\u003e\u003cem\u003e-\u003c/em\u003e\u003cem\u003eThird Edition Manual (WISC-III).\u003c/em\u003e New York: Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1997). \u003cem\u003eWechsler Adult Intelligence Scale (3rd ed.) (WAIS-III)\u003c/em\u003e. San Antonio, TX: The Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1999). \u003cem\u003eWechsler Abbreviated Intelligence Scale\u003c/em\u003e \u003cem\u003e(WAIS).\u003c/em\u003e San Antonio, TX: Pearson Publishers.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (1999). \u003cem\u003eThe Wechsler Abbreviated Scale of\u003c/em\u003e \u003cem\u003eIntelligence-UK (WASI).\u003c/em\u003e San Antonio, TX: The Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (2002). \u003cem\u003eWechsler individual achievement test II.\u003c/em\u003e San Antonio, TX: Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (2003). \u003cem\u003eWISC-IV technical and interpretive\u003c/em\u003e \u003cem\u003emanual.\u003c/em\u003e San Antonio, TX: Psychological Corporation.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (2004). \u003cem\u003eWechsler Preschool and Primary School\u003c/em\u003e \u003cem\u003eIntelligence III (WPPSI-III) Australian: Administration and\u003c/em\u003e \u003cem\u003escoring manual. \u003c/em\u003eNSW: Harcourt Assessment.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (2009). \u003cem\u003eWechsler Individual Achievement Scales,\u003c/em\u003e \u003cem\u003eIII (WIAS-III).\u003c/em\u003e San Antonio, TX: The Pearson Publishers.\u003c/li\u003e\n\u003cli\u003eWechsler, D. (2011). \u003cem\u003eWechsler Abbreviated Scale of Intelligence Second Edition\u003c/em\u003e. San Antonio, TX: Pearson.\u003c/li\u003e\n\u003cli\u003eWei, X., Blackorby, J., \u0026amp; Schiller, E. (2011). Growth in reading achievement in a national sample of students with disabilities ages 7 to 17. \u003cem\u003eExceptional Children,\u003c/em\u003e \u003cem\u003e78\u003c/em\u003e, 89-106.\u003c/li\u003e\n\u003cli\u003eWei, X., Yu, J. W., Shattuck, P., McCracken, M., \u0026amp; Blackorby, J. (2012). Science, technology, engineering, and mathematics (STEM) participation among college students with an autism spectrum disorder. \u003cem\u003eJournal of Autism and\u003c/em\u003e \u003cem\u003eDevelopmental Disorders,\u003c/em\u003e \u003cem\u003e43\u003c/em\u003e, 1-8.\u003c/li\u003e\n\u003cli\u003eWei, X., Lenz, K. B., \u0026amp; Blackorby, J. (2013). Math growth trajectories of students with disabilities: Disability category, gender, racial, and socioeconomic status differences from ages 7 to 17. \u003cem\u003eRemedial and Special Education, 34\u003c/em\u003e(3), 154-165. \u003c/li\u003e\n\u003cli\u003eWei, X., Elizabeth, R. A., Jennifer, W., Mary, W., \u0026amp; Spiker, D. (2014). Reading and math achievement profiles and longitudinal growth trajectories of children with an autism spectrum disorder. \u003cem\u003eAutism, 19\u003c/em\u003e(2), 200-210.\u003c/li\u003e\n\u003cli\u003e*Williams, D. L., Goldstein, G., \u0026amp; Minshew, N. J. (2006). The profile of memory function in children with autism. \u003cem\u003eNeuropsychology,\u003c/em\u003e \u003cem\u003e20\u003c/em\u003e(1), 21-29.\u003c/li\u003e\n\u003cli\u003eWoodcock, R. W., \u0026amp; Johnson, M. B. (1977). \u003cem\u003eWoodcock-\u003c/em\u003e \u003cem\u003eJohnson Psycho-Educational Battery. \u003c/em\u003eAllen, TX: DLM Teaching Resources.\u003c/li\u003e\n\u003cli\u003eYakubova, G., Hughes, E. H., \u0026amp; Baer, B. L. (2020). Supporting\u003c/li\u003e\n\u003cli\u003estudents with ASD in mathematics learning using video-based concrete-representational abstract sequencing instruction. \u003cem\u003ePreventing School Failure: Alternative Education for Children and Youth\u003c/em\u003e, \u003cem\u003e64\u003c/em\u003e(1), 12-18.\u003c/li\u003e\n\u003cli\u003e*Zaidman-Zait, A., Mirenda, P., Szatmari, P., Duku, E., \u0026amp; Elsabbagh, M., et al. (2020). Profiles and predictors of academic and social school functioning among children with autism spectrum disorder. \u003cem\u003eJournal of Clinical Child and\u003c/em\u003e \u003cem\u003eAdolescent Psychology, 50\u003c/em\u003e(5), 656-668.\u003c/li\u003e\n\u003cli\u003eZheng, S., Hume, K., Able, H., Bishop, S. L., \u0026amp; Boyd, B. A. (2020). Exploring Developmental and Behavioral Heterogeneity among Preschoolers with ASD: A Cluster Analysis on Principal Components. \u003cem\u003eAutism Research\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(5), 796-809.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Meta-analysis results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 350px;\"\u003e\n \u003cp\u003eEstimates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003ek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eASD vs. Norm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eMath proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.565, -.089]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eModerators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eADOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-1.607, .605]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.030, .609]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePublication year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.020, .021]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.032, .066]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.036, .060]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.047, .059]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eInteraction effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.580, .364]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.558\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.360, .429]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.144, .151]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eMath variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.112, .228]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eModerators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eADOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.209, .037]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.006, .030]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePublication year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.007, .005]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.772\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.008, -.0002]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.008, .005]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.006, .008]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eInteraction effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.095, .121]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.445, -.031]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.278, .051]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eASD vs. TD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eMath proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.962, -.390]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eModerators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eADOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.948, .887]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.924\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.011, .050]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePublication year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.055, -.006]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.022, .082]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ of TD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.-.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.059, -.014]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.013, .094]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ of TD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.047, -002]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.013, .077]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal IQ of TD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.082, -.027]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eInteraction effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.274, -.045]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.916, .744]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.918, .280]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eMath variability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[.207, .417]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eModerators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eADOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.1210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.175, .586]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.023, .020]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePublication year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.004, .017]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.016, .022]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ of TD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.024, .024]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.014, .007]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ of TD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.002, .017]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal IQ of ASD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.016, .007]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.389\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal IQ of TD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.010, .023]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eInteraction effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFull-scale IQ * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.042, .262]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVerbal IQ * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.183, .301]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNon-verbal * Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e[-.281, .274]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"autism spectrum disorder, mathematical ability, intelligence","lastPublishedDoi":"10.21203/rs.3.rs-5667808/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5667808/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe fundamental characteristics of mathematical ability in individuals with Autism Spectrum Disorder (ASD), specifically proficiency level and variability, remain inadequately understood. This meta-analysis reveals that individuals with ASD exhibit significantly lower math scores (Hedge\u0026rsquo;s g = -0.181/-0.592) and greater variability (natural logarithm of variability ratio, \u003cem\u003elnVR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.179/0.272) compared to the general population, as represented by norms of standardized math tests (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15; 3,051 participants) and typically developing (TD) control groups (2,351 participants). Group discrepancies in proficiency and variability were moderated by intelligence, age, or their interactions. The moderate math-intelligence relationship in the population with ASD provides a theoretical framework for studying their math abilities. Additionally, the discrepancy in math proficiency between the ASD and TD groups increases over the past four decades. These findings underscore the need for sustained, individualized mathematical education for individuals with ASD, and the importance of investigating the developmental trajectories of mathematical skills in ASD.\u003c/p\u003e","manuscriptTitle":"The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-06 14:40:09","doi":"10.21203/rs.3.rs-5667808/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-human-behaviour","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nathumbehav","sideBox":"Learn more about [Nature Human Behaviour](http://www.nature.com/nathumbehav/)","snPcode":"","submissionUrl":"","title":"Nature Human Behaviour","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8e8125b4-2041-4496-9ca3-14576a979605","owner":[],"postedDate":"January 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42327724,"name":"Social science/Psychology/Human behaviour"},{"id":42327725,"name":"Social science/Education"}],"tags":[],"updatedAt":"2026-02-19T08:06:50+00:00","versionOfRecord":{"articleIdentity":"rs-5667808","link":"https://doi.org/10.1038/s41562-025-02384-2","journal":{"identity":"nature-human-behaviour","isVorOnly":false,"title":"Nature Human Behaviour"},"publishedOn":"2026-02-18 05:00:00","publishedOnDateReadable":"February 18th, 2026"},"versionCreatedAt":"2025-01-06 14:40:09","video":"","vorDoi":"10.1038/s41562-025-02384-2","vorDoiUrl":"https://doi.org/10.1038/s41562-025-02384-2","workflowStages":[]},"version":"v1","identity":"rs-5667808","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5667808","identity":"rs-5667808","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}